Accurate drug-target affinity (DTA) prediction holds significant potential in the field of artificial intelligence (AI)-based drug discovery. However, existing methods primarily operate at a single scale, specifically at the macro (residue) scale for target proteins and the micro (atom) scale for drugs, which limits their ability to provide information at micro (atom) scale for targets and macro (functional group, FG) scale for drugs. This limitation hinders a comprehensive understanding of the binding patterns and properties of drug-target pairs. In this paper, we propose a progressive Macro-to-Micro 3D Modeling Network (M²N) that enables macro (residue/FG) to micro (atom) scale unified modeling, termed cross-scale, to predict DTA. Specifically, M²N operates drugs by learning their chemical properties and structural characteristics from a 3D FG graph to a 3D atom graph. Correspondingly, M²N encodes proteins from a 3D residue graph to a 3D atom graph to exploit their sequence, evolutionary, and geometric representations. Such cross-scale 3D modeling scheme allows for coarse-to-fine embedding optimization, followed by an adaptive fusion module to dynamically integrate the refined features by end-to-end learning. Extensive experiments on two datasets indicate that M²N not only outperforms state-of-the-art methods under various conditions, but also provides a new paradigm for target and drug unified modeling.

While metabolic imaging can facilitate early diagnosis by revealing physiological changes of lesions, it is limited by high cost, high radiation risk, and potential renal impairment. Thus, developing an effective approach for Anatomical-to-Metabolic Image Synthesis (A2MIS) is highly required. However, existing methods are heavily hindered by the gap between distinct domains, and fail to provide a confidence score for the synthesized images, severely restricting their clinical applications. Here, we propose a novel Residual-guided Latent Brownian-bridge Co-Diffusion (RLBCD) model for A2MIS. Specifically, RLBCD starts with a co-diffusion process that leverages a residual diffusion branch to capture inter-domain differences, which are injected into an enhanced diffusion branch to maximally reconstruct modality-specific details. Furthermore, to explore desired residual guidance, we investigate the encoder and decoder features in diffusion models, and accordingly design a Hybrid-Granularity Fusion to integrate consistent semantics and complementary information for fine-grained reconstruction. Additionally, a latent consistency score is developed to enhance the restoration of modality-specific information, which also serves as an indicator of the inherent confidence of the synthesized images. Extensive experiments conducted on five public and in-house datasets demonstrate that RLBCD not only outperforms state-of-the-art methods for A2MIS, but also is valuable for downstream clinic applications.

Spatial transcriptomics is an emerging technology that allows for analysis of cellular and molecular heterogeneity at spatial resolution. The accurate identification of pathological regions in spatial transcriptomics data is essential for understanding tissue heterogeneity and disease progression. We introduce UPSST, a comprehensive framework that integrates tissue morphology, imputes gene expression, and clusters spatial regions using a graph attention neural network (GAT). UPSST was evaluated across multiple spatial transcriptomics datasets, achieving high performance, such as achieving an Adjusted Rand Index (ARI) of 0.737 and a Fowlkes-Mallows Index (FMI) of 0.818 on slice 151671 of the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset. These results highlight the robustness and precision of our approach in identifying pathology domains. Additionally, UPSST facilitates downstream analyses such as differential and enrichment analysis, which are crucial for deriving biological insights. In conclusion, UPSST offers a powerful and reliable tool for spatial transcriptomics analysis, advancing the identification of pathological regions with high accuracy.

Immunotherapy with immune checkpoint inhibitors (ICIs) is increasingly used to treat various tumor types. Determining patient responses to ICIs presents a significant clinical challenge. Although components of the tumor microenvironment (TME) are used to predict patient outcomes, comprehensive assessments of the TME are frequently overlooked. Using a top-down approach, the TME was divided into five layers-outcome, immune role, cell, cellular component, and gene. Using this structure, a neural network called TME-NET was developed to predict responses to ICIs. Model parameter weights and cell ablation studies were used to investigate the influence of TME components. The model was developed and evaluated using a pan-cancer cohort of 948 patients across four cancer types, with Area Under the Curve (AUC) and accuracy as performance metrics. Results show that TME-NET surpasses established models such as support vector machine and k-nearest neighbors in AUC and accuracy. Visualization of model parameter weights showed that at the cellular layer, Th1 cells enhance immune responses, whereas myeloid-derived suppressor cells and M2 macrophages show strong immunosuppressive effects. Cell ablation studies further confirmed the impact of these cells. At the gene layer, the transcription factors STAT4 in Th1 cells and IRF4 in M2 macrophages significantly affect TME dynamics. Additionally, the cytokine-encoding genes IFNG from Th1 cells and ARG1 from M2 macrophages are crucial for modulating immune responses within the TME. Survival data from immunotherapy cohorts confirmed the prognostic ability of these markers, with p-values <0.01. In summary, TME-NET performs well in predicting immunotherapy responses and offers interpretable insights into the immunotherapy process. It can be customized at https://immbal.shinyapps.io/TME-NET.

Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.

Whole Slide Images (WSIs) are paramount in the medical field, with extensive applications in disease diagnosis and treatment. Recently, many deep-learning methods have been used to classify WSIs. However, these methods are inadequate for accurately analyzing WSIs as they treat regions in WSIs as isolated entities and ignore contextual information. To address this challenge, we propose a novel Dual-Granularity Cooperative Diffusion Model (DCDiff) for the precise classification of WSIs. Specifically, we first design a cooperative forward and reverse diffusion strategy, utilizing fine-granularity and coarse-granularity to regulate each diffusion step and gradually improve context awareness. To exchange information between granularities, we propose a coupled U-Net for dual-granularity denoising, which efficiently integrates dual-granularity consistency information using the designed Fine- and Coarse-granularity Cooperative Aware (FCCA) model. Ultimately, the cooperative diffusion features extracted by DCDiff can achieve cross-sample perception from the reconstructed distribution of training samples. Experiments on three public WSI datasets show that the proposed method can achieve superior performance over state-of-the-art methods. The code is available at https://github.com/hemo0826/DCDiff.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains information on tumor morphology and physiology for breast cancer diagnosis and treatment. However, this technology requires contrast agent injection with more acquisition time than other parametric images, such as T2-weighted imaging (T2WI). Current image synthesis methods attempt to map the image data from one domain to another, whereas it is challenging or even infeasible to map the images with one sequence into images with multiple sequences. Here, we propose a new approach of cross-parametric generative adversarial network (GAN)-based feature synthesis (CPGANFS) to generate discriminative DCE-MRI features from T2WI with applications in breast cancer diagnosis. The proposed approach decodes the T2W images into latent cross-parameter features to reconstruct the DCE-MRI and T2WI features by balancing the information shared between the two. A Wasserstein GAN with a gradient penalty is employed to differentiate the T2WI-generated features from ground-truth features extracted from DCE-MRI. The synthesized DCE-MRI feature-based model achieved significantly (p = 0.036) higher prediction performance (AUC = 0.866) in breast cancer diagnosis than that based on T2WI (AUC = 0.815). Visualization of the model shows that our CPGANFS method enhances the predictive power by levitating attention to the lesion and the surrounding parenchyma areas, which is driven by the interparametric information learned from T2WI and DCE-MRI. Our proposed CPGANFS provides a framework for cross-parametric MR image feature generation from a single-sequence image guided by an information-rich, time-series image with kinetic information. Extensive experimental results demonstrate its effectiveness with high interpretability and improved performance in breast cancer diagnosis.

Assessments of multiple clinical indicators based on radiomic analysis of magnetic resonance imaging (MRI) are beneficial to the diagnosis, prognosis and treatment of breast cancer patients. Many machine learning methods have been designed to jointly predict multiple indicators for more accurate assessments while using original clinical labels directly without considering the noisy and redundant information among them. To this end, we propose a multilabel learning method based on label space dimensionality reduction (LSDR), which learns common and task-specific features via graph regularized nonnegative matrix factorization (CTFGNMF) for the joint prediction of multiple indicators in breast cancer. A nonnegative matrix factorization (NMF) is adopted to map original clinical labels to a low-dimensional latent space. The latent labels are employed to exploit task correlations by using a least square loss function with [Formula: see text]-norm regularization to identify common features, which help to improve the generalization performance of correlated tasks. Furthermore, task-specific features were retained by a multitask regression formulation to increase the discrimination power for different tasks. Common and task-specific features are incorporated by dynamic graph Laplacian regularization into a unified model to learn complementary features. Then, a multilabel classification is built to predict multiple clinical indicators including human epidermal growth factor receptor 2 (HER2), Ki-67, and histological grade. Experimental results show that CTFGNMF achieves AUCs of 0.823, 0.691 and 0.776 in the three indicator predictions, outperforming other counterparts that consider only task-independent features or common features. It indicates CTFGNMF is a promising application for multiple classification tasks in breast cancer.

The clinical management and decision-making process related to breast cancer are based on multiple histological indicators. This study aims to jointly predict the Ki-67 expression level, luminal A subtype and histological grade molecular biomarkers using a new deep multitask learning method with multiparametric magnetic resonance imaging. A multitask learning network structure was proposed by introducing a common-task layer and task-specific layers to learn the high-level features that are common to all tasks and related to a specific task, respectively. A network pretrained with knowledge from the ImageNet dataset was used and fine-tuned with MRI data. Information from multiparametric MR images was fused using the strategy at the feature and decision levels. The area under the receiver operating characteristic curve (AUC) was used to measure model performance. For single-task learning using a single image series, the deep learning model generated AUCs of 0.752, 0.722, and 0.596 for the Ki-67, luminal A and histological grade prediction tasks, respectively. The performance was improved by freezing the first 5 convolutional layers, using 20% shared layers and fusing multiparametric series at the feature level, which achieved AUCs of 0.819, 0.799 and 0.747 for Ki-67, luminal A and histological grade prediction tasks, respectively. Our study showed advantages in jointly predicting correlated clinical biomarkers using a deep multitask learning framework with an appropriate number of fine-tuned convolutional layers by taking full advantage of common and complementary imaging features. Multiparametric image series-based multitask learning could be a promising approach for the multiple clinical indicator-based management of breast cancer.

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However, deep learning resulting with black-box models, which often breaks down when forced to make predictions about data for which limited supervised information is available and lack inter-pretability, still is a major barrier for clinical integration. In this work, we hereby propose a semantic-powered explainable model-free few-shot learning scheme to quickly and precisely diagnose COVID-19 with higher reliability and transparency. Specifically, we design a Report Image Explanation Cell (RIEC) to exploit clinically indicators derived from radiology reports as interpretable driver to introduce prior knowledge at training. Meanwhile, multi-task collaborative diagnosis strategy (MCDS) is developed to construct N-way K-shot tasks, which adopts a cyclic and collaborative training approach for producing better generalization performance on new tasks. Extensive experiments demonstrate that the proposed scheme achieves competitive results (accuracy of 98.91%, precision of 98.95%, recall of 97.94% and F1-score of 98.57%) to diagnose COVID-19 and other pneumonia infected categories, even with only 200 paired CXR images and radiology reports for training. Furthermore, statistical results of comparative experiments show that our scheme provides an interpretable window into the COVID-19 diagnosis to improve the performance of the small sample size, the reliability and transparency of black-box deep learning models. Our source codes will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/SPEMFSL-Diagnosis-COVID-19.

More attention has been paid to the precision and personalized treatment of breast cancer, which is a primary risk factor that threatens the females lives. It is momentous for diagnosis, analysis and therapy of tumors to lucubrate breast intratumor heterogeneity. We propose a DCE-MRI dynamic mode based self-supervised dual attention deep clustering network (DADCN) which is utilized to achieve the individual precise segmentation of breast intratumor heterogeneity region in this paper. The specific representations learned by the graph attention network are consciously combined with the deep abstract features extracted from the deep convolutional neural network. Then the structural information of the voxel in breast tumor is mined by spreading on the graph. The model is self-supervised by dual relative loss and residual loss and the clustering graph is measured by graph cut loss. We also employ Pearson, Spearman and Kendall analysis to evaluate degree of correlation between clustering results and intratumor heterogeneity represented by molecular typing. We ultimately detect that the degree of intratumor heterogeneity is automatically determined via segmentation of the heterogeneity region, to accomplish the noninvasive individual molecular typing prediction of breast cancer. The number of clusters in breast intratumor heterogeneity region is an independent biomarker for the diagnosis of benign and malignant tumors and prediction of basal-like molecular typing.

Magnetic resonance imaging (MRI) is a widely used diagnostic technique in the initial evaluation of patients with primary brain tumor. Automatic segmentation algorithms for brain tumor plays an important role in diagnosis of tumor subregions and treatments for patients. In this paper, we propose a novel 3D convolutional neural network for segmentation of brain tumor, which is based on the traditional encoder-decoder architecture and inspired by multi-scale strategy. The proposed network employs multi-scale strategy with the designed dualencoder structure which can be considered as two scales streams to extract features from two scales inputs respectively. To integrate the output features from two scales encoders, a multiscale attention is model designed to weight and integrated the multi-scale features. Moreover, with the purpose of modeling long-range feature dependencies more efficiently, hidden features from different positions of stream are also fused and then utilized by attention mechanism in each scale encoder. To validate the performance of the proposed network, we conduct experiments on the brain tumor segmentation dataset BraTS2019 against several state-of-art methods. The results show that proposed method has the effective and competitive performance in brain tumor segmentation.

Magnetic resonance imaging (MRI) is an important medical diagnosis technique in clinical diagnosis, while the quality of MR images is always damaged by the noise which is caused in the image acquisition process. In the classic image denoising methods, how to design an excellent regularizer with the prior knowledge of image is the key to solve the denoising problem. In this work, we introduce the deep neural regularizer for the MRI denoising tasks, the deep neural regularizer is made up of neural network structure and objective function, similar to the classic regularizer, both of these two parts are designed with the prior knowledge of image. The proposed neural network has three main parts: encoder network, decoder network and skip connections, the encoder network which consists of five down-sampling blocks is enforced to deeply extract low-resolution or highly-abstract MR image features, similar to the encoder network architecture, the decoder network is made up of five up-sampling blocks and is enforced to restore high-resolution MR image features. To generate more finer image features, we also use skip connections to transmit the abstract information from encoder to decoder directly. The objective function consists of data fidelity term and image quality penalty term, specifically, to enforce the capability of data fidelity term, we add the self-designed image structural consistency calculation to data fidelity term besides only calculating the image consistency over image pixels with mean squared error. Meanwhile, to guide the network generate more clearer image and reduce noise information, with the prior knowledge of image sharpness, an image quality penalty term which calculates the MR image sharpness is also added to the objective function. Experimental results over the simulated MRI data and real clinical data demonstrate the proposed network can achieve superior performance compared with other methods in terms of peak signal to noise ratio, structure similarity index, image average gradient and image information entropy.

OBJECTIVE: Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic analysis. METHODS: We jointly predicted Ki-67 and tumor grade with a multitask learning framework by separately utilizing radiomics from tumor MRI series. Additionally, we showed how multitask learning models (MTLs) could be extended to combined radiomics from the MRI series for a better prediction based on the assumption that features from different sources of images share common patterns while providing complementary information. Tumor radiomic analysis was performed with morphological, statistical and textural features extracted on the DWI and dynamic contrast-enhanced MRI (DCE-MRI) series of the precontrast and subtraction images, respectively. RESULTS: Joint prediction of Ki-67 status and tumor grade on MR images using the MTL achieved performance improvements over that of single-task-based predictive models. Similarly, for the prediction tasks of Ki-67 and tumor grade, the MTL for combined precontrast and apparent diffusion coefficient (ADC) images achieved AUCs of 0.811 and 0.816, which were significantly better than that of the single-task- based model with p values of 0.005 and 0.017, respectively. CONCLUSION: Mapping MRI radiomics to two related clinical indicators improves prediction performance for both Ki-67 expression level and tumor grade. SIGNIFICANCE: Joint prediction of indicators by multitask learning that combines correlations of MRI radiomics is important for optimal tumor therapy and treatment because clinical decisions are made by integrating multiple clinical indicators.

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

Motivation: Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number. Results: We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms. Availability and implementation: The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures. Results: Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster. Availability and implementation: The main program is available at https://github.com/lykaust15/DLBI. Supplementary information: Supplementary data are available at Bioinformatics online.

Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently,$^1$H-detected experiments are the dominant NMR technique used due to the high sensitivity of$^1$H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of$^{13}$C nuclei. Consequently,$^{13}$C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins. In this paper, we propose the first spin system forming method for$^{13}$C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two$^{13}$C-detected spectra, CBCACON, and CBCANCO. Our method picks slices from the more trusted spectrum and uses them as feedback to direct the slice picking in the less trusted one. This feedback leads to picking the accurate slices that consequently helps to form better spin systems. We tested our method on a real dataset of ‘Ubiquitin’ and a benchmark simulated dataset consisting of 12 proteins. We fed our spin systems as inputs to a genetic algorithm to generate the chemical shift assignment, and obtained 92 percent correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86 percent and an average precision of 88 percent. Finally, our chemical shift assignment of Ubiquitin was given as an input to CS-ROSETTA server that generated structures close to the experimentally determined structure.

In this paper, a detailed study on a composite derivative is performed. The composite derivative, which is formed from the combination of fractional integration and derivative and performs a $90^\circ$ phase shift as the traditional first derivative does, is applied to edge detection and the results are analyzed, emphasizing the compromise ability between selectivity and noise suppression. Both objective and subjective comparisons with other edge detectors are carried out, including evaluations through the use of the benchmark Berkeley Segmentation Dataset (BSDS500). In contrast with the classical first-order derivative, the composite derivative is order-steerable; one can adjust the orders of fractional integration and derivative to tune magnitude characteristic and reach a compromise between sensitivity to noise and detection accuracy.

Operation with adjuvant chemotherapy is still the principal means to treat Ovarian cancer. Identifying Ovarian Cancer Chemotherapy Response (OCCR) relevant genes and describe their interactions is thus an important issue. However the problems of high dimensional microarray data and the scarcity of biological priors make building a complete OCCR biological network intractable. To this end, we combine liquid association (LA) algorithm with biological knowledgebase searching to identify OCCR relevant gene clique and describe their interactions. Rather than trying to build a gene network, our approach focus on identifying OCCR relevant gene cliques and then patching them up. Statistical analysis and biological validation show that the identified gene cliques play important roles in tumorigenesis, immunity, cells proliferation and migration etc and significantly OCCR relevant. More importantly, the connection of independent gene cliques is established and the associations of genes are described. Methodologically, the proposed method avoids the problem of complex computation, relies only on available biological priors and provides a novel way to build gene network.

Breast cancer is the most common malignant disease in women. Mammographic mass retrieval system can help radiologists to improve the diagnostic accuracy by retrieving biopsy-proven masses which are similar with the diagnostic ones. However, although screening mammograms usually consists of two-view(MLO and CC) mammography of the same breast, most breast CAD systems incorporate with image retrieval techniques are based on a single-view principle where query ROI within a view is analyzed independently. In this paper, a mammographic mass retrieval approach based on multi-view information is proposed. In this work, the query example is a multi-view(MLO and CC) mass pair instead of the single view mass in the traditional image retrieval framework. In the experiments, several visual features are used for retrieval evaluation. Both distance similarity measures, such as Euclidean distance, and k-NN regression model based non-distance similarity measures are used for comparison. Experimental study was carried out on a database with 126 biopsy-proven masses(63 mass pairs). Preliminary results showed that multi-view based retrieval approach achieves better retrieval accuracy than single-view based one, especially for the k-NN regression model based similairy metric.

An approach of medical image decomposition and texture feature extraction based on the bidimensional empirical mode decomposition(BEMD), which can decompose the image into a set of functions denoted intrinsic mode functions (IMF) and a residue, was presented. Features extracted were the mean and standard deviation of the amplitude matrix, phase matrix and instantaneous frequency matrix of the IMFs and their Hilbert transformations. The extracted features were used for medical image retrieval. Moreover, according to the spatial relationship between local extrema points, a new boundary processing method based on clustering algorithm was proposed. In order to evaluate the proposed BEMD-based feature, we also presented a new multiscale fractal dimension feature. Preliminary comparison experimental results showed that the retrieval results of the BEMD-based feature were encouraged.

Registering features in multiple mammographic views is an important technique to improve breast cancer detection rate. However, nonrigid breast deformation during X-ray imaging poses a severe challenge to the conventional 2D registration methods. We present a method that utilizes a 3D model to facilitate two-view registration by predicting breast deformation. At first, a finite element model of a breast is constructed using its MRIs. The model is capable of simulating both compression and decompression. Feature registration is then accomplished through a series of projections and compression-decompression operations. Experiments using real patient data demonstrate that a mammographic feature can be successfully registered from one view to another.

A texture image can be characterized by its fractal feature, and hence, the fractal features can be used as the texture signature to retrieve the images. In this paper, based on the independence of fractal parameters, we improve the three-dimensional histogram into a composite index, in order to reduce computational complexity. Furthermore, under the same computational cost, we make the comparison between the proposed composite index and fractal-dimension-related features, and experimental results on a database of 640 texture images indicate that the proposed composite index outperforms fractal-dimension-related features

Directional features are extremely important in diagnostic imaging. They can be used either in the searching for important indications of abnormality or for the reference in image alignment. The wavelet transform (WT) as an efficient method for multiresolution representation has been used extensively in medical imaging. However, from the view point of directional selectivity it is not so efficient in feature extraction. In this paper, an analysis and comparison of directional selectivity of wavelet transforms with three different frequency decompositions were undertaken. WTs with radial-angular decomposition were used for two medical imaging problems: mass detection in digital mammography and lung nodule detection in chest radiography. The results demonstrated its efficiency.

Multi-resolution and multi-orientation are part of the biological mechanism of human visual system. The interest in multi-resolution image analysis has been growing rapidly. Comparatively, however, the multi-orientation method has received less attention. Based on the relationship between multifrequency channel decomposition and the wavelet model developed by Mallat [1989], a combined multiresolution/multi-orientation representation by directional wavelet transform (DWT) is introduced in this paper. Because of its high orientation selectivity, the DWT allows the processing of directional information more efficiently. It is applied successfully to feature enhancement and extraction of X-ray mammographic images.

This paper presents a decision feedback neural network (NN) coherent receiver scheme for continuous phase modulation based on frequency domain. Through decision feedback pre-processing, the effect of the previous transmitted symbols can be removed for the present symbol decision. By employing a Karhunen-Loeve transform (KLT) or discrete cosine transform (DCT), the input data number of neural networks can be reduced significantly. To obtain more sufficient convergence of neural networks a modified "delta-bar-delta" BP learning algorithm is proposed. Despite the low complexity in NN training and implementation, computer simulation results show that our NN receivers can achieve near optimal demodulation performance.

In this paper, a new competitive learning algorithm based on the partial distortion theorem is proposed for the on-line vector quantizer design. The novel algorithm is called partial-distortion-equivalent competitive learning (PDECL) algorithm, which aims at making the partial distortions for each neuron (code-vector) be uniform to overcome the neuron underuse problem as well as to minimize the average distortion for the designed vector quantizer. Compared with the Kohonen learning algorithm (KLA), the frequency-sensitive competitive learning (FSCL) algorithm and the soft competition scheme (SCS) algorithm, the PDECL consistently shows the better performance than all of them and the LBG algorithm for the design of vector quantizers with different codebook sizes especially when the codebook size is large enough.>