Lung cancer continues to be the leading cause of cancer-related deaths globally. Early detection and diagnosis of pulmonary nodules are essential for improving patient survival rates. Although previous research has integrated multimodal and multi-temporal information, outperforming single modality and single time point, the fusion methods are limited to inefficient vector concatenation and simple mutual attention, highlighting the need for more effective multimodal information fusion. To address these challenges, we introduce a Dual-Graph Spatiotemporal Attention Network, which leverages temporal variations and multimodal data to enhance the accuracy of predictions. Our methodology involves developing a Global-Local Feature Encoder to better capture the local, global, and fused characteristics of pulmonary nodules. Additionally, a Dual-Graph Construction method organizes multimodal features into inter-modal and intra-modal graphs. Furthermore, a Hierarchical Cross-Modal Graph Fusion Module is introduced to refine feature integration. We also compiled a novel multimodal dataset named the NLST-cmst dataset as a comprehensive source of support for related research. Our extensive experiments, conducted on both the NLST-cmst and curated CSTL-derived datasets, demonstrate that our DGSAN significantly outperforms state-of-the-art methods in classifying pulmonary nodules with exceptional computational efficiency.

Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to its accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address this issue, we develop an end-to-end framework called BS-LDM, designed to effectively suppress bone in high-resolution CXR images. This framework is based on conditional latent diffusion models and incorporates a multi-level hybrid loss-constrained vector-quantized generative adversarial network which is crafted for perceptual compression, ensuring the preservation of details. To further enhance the framework's performance, we utilize offset noise in the forward process, and a temporal adaptive thresholding strategy in the reverse process. These additions help minimize discrepancies in generating low-frequency information of soft tissue images. Additionally, we have compiled a high-quality bone suppression dataset named SZCH-X-Rays. This dataset includes 818 pairs of high-resolution CXR and soft tissue images collected from our partner hospital. Moreover, we processed 241 data pairs from the JSRT dataset into negative images, which are more commonly used in clinical practice. Our comprehensive experiments and downstream evaluations reveal that BS-LDM excels in bone suppression, underscoring its clinical value.

Intracerebral hemorrhage (ICH) necessitates precise and efficient segmentation of hemorrhagic regions in head computed tomography (CT) scans to facilitate timely clinical decisions. To address challenges such as irregular shapes of hematomas, unclear lesion boundaries, and the scarcity of annotated data, we introduce the ICH-PFNet, a text-guided segmentation framework specifically designed for ICH imaging that operates without prompts. The Mamba Pyramid Downsampling module ensures robust multi-scale feature extraction, while the GCS-CLIP fusion mechanism enhances semantic consistency through batch-level contrastive similarity. The Enhanced SAM module provides automatic spatial guidance and convolution-based sparse embeddings to eliminate manual input. Furthermore, a Feature Pyramid Network combined with a Group Aggregation Bridge enhances multi-scale feature fusion and refines boundaries. Our model showed superior performance in segmenting small and structurally complex hemorrhages by using a private CT dataset. These results highlight its potential for integration into automated ICH assessment workflows. The code is available at https://github.com/Hzchzc123/ICH-CMNet.

Multi-organ medical segmentation is a crucial component of medical image processing, essential for doctors to make accurate diagnoses and develop effective treatment plans. Despite significant progress in this field, current multi-organ segmentation models often suffer from inaccurate details, dependence on geometric prompts and loss of spatial information. Addressing these challenges, we introduce a novel model named CRISP-SAM2 with CR oss-modal Interaction and Semantic Prompting based on SAM2. This model represents a promising approach to multi-organ medical segmentation guided by textual descriptions of organs. Our method begins by converting visual and textual inputs into cross-modal contextualized semantics using a progressive cross-attention interaction mechanism. These semantics are then injected into the image encoder to enhance the detailed understanding of visual information. To eliminate reliance on geometric prompts, we use a semantic prompting strategy, replacing the original prompt encoder to sharpen the perception of challenging targets. In addition, a similarity-sorting self-updating strategy for memory and a mask-refining process is applied to further adapt to medical imaging and enhance localized details. Comparative experiments conducted on seven public datasets indicate that CRISP-SAM2 outperforms existing models. Extensive analysis also demonstrates the effectiveness of our method, thereby confirming its superior performance, especially in addressing the limitations mentioned earlier. Our code is available at: https://github.com/YU-deep/CRISP_SAM2.git.

Accurate runoff prediction is essential for effective water resource management, particularly in addressing flood control and monitoring drought conditions. However, the diverse nature of land types and varying climate conditions often complicate this task, requiring frequent adaptations to prediction models for local applications. Existing methods primarily focus on modeling for individual regions, while regional runoff prediction models cannot often learn long-term patterns, limiting their regional adaptability. To overcome this challenge, we present the temporal fusion runoff network (TFRN), a new framework designed to enhance long short-term memory (LSTM) models by enabling them to incorporate distant historical information. This innovation offers a promising framework for regional runoff prediction by enhancing model performance and minimizing computational demands. In this study, the proposed TFRN utilizes convolutional networks to extract and integrate both long-term and short-term trends from input sequences, and by merging the strengths of LSTM and Transformer architectures, TFRN achieves a thorough integration of historical data. Specifically, our method employs convolutional networks across both time and frequency domains to capture multi-scale features. Within the Transformer component, we introduce an adaptive fusion module to improve the integration of historical information. We validated the effectiveness of our model using two extensive hydrological datasets for a 7-day runoff prediction task. The results underscore the superiority of our approach, demonstrating its advantages over several leading methods. The source code is available at https://github.com/redtea-code/TFRN.

The fusion of multimodal medical data is crucial for helping doctors make accurate treatment decisions. For example, combining Computed Tomography Pulmonary Angiography (CTPA) with Electronic Health Records (EHR) can significantly improve the accuracy of Pulmonary Embolism (PE) detection, thereby increasing patient survival rates. Although multimodal learning has advantages in PE diagnosis, the heterogeneity of multimodal data poses a significant challenge to accurate diagnosis. The natural semantic and structural differences between data modalities make it difficult to effectively integrate their information. In addition, within a single modality, the existence of redundant and irrelevant information introduces unnecessary variability, making the data more complex, and making stable diagnosis challenging. To address these issues, we propose a new framework called CCLNet, which includes a contrastive learning component for addressing inter-modality heterogeneity and a causal learning component for handling intra-modality heterogeneity. Specifically, we achieve precise alignment between visual and tabular modalities by using global-level information to soften labels during contrastive learning. In addition, by using causal intervention methods to eliminate the influence of heterogeneous factors within the modality, we can accurately reveal the causal relationship between features and targets, thereby improving the accuracy and stability of the model. Experimental results demonstrate that our method performs excellently, achieving the best results. Our code is available at https://github.com/LeavingStarW/CLPE.

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics.

COVID-19, caused by the highly contagious SARS-CoV-2 virus, is distinguished by its positive-sense, single-stranded RNA genome. A thorough understanding of SARS-CoV-2 pathogenesis is crucial for halting its proliferation. Notably, the 3C-like protease of the coronavirus (denoted as$3CL^{pro}$) is instrumental in the viral replication process. Precise delineation of$3CL^{pro}$cleavage sites is imperative for elucidating the transmission dynamics of SARS-CoV-2. While machine learning tools have been deployed to identify potential$3CL^{pro}$cleavage sites, these existing methods often fall short in terms of accuracy. To improve the performances of these predictions, we propose a novel analytical framework, the Transformer and Deep Forest Fusion Model (TDFFM). Within TDFFM, we utilize the AAindex and the BLOSUM62 matrix to encode protein sequences. These encoded features are subsequently input into two distinct components: a Deep Forest, which is an effective decision tree ensemble methodology, and a Transformer equipped with a Multi-Level Attention Model (TMLAM). The integration of the attention mechanism allows our model to more accurately identify positive samples, thus enhancing the overall predictive performance. Evaluation on a test set demonstrates that our TDFFM achieves an accuracy of 0.955, an AUC of 0.980, and an F1-score of 0.367, substantiating the model's superior prediction capabilities.

Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts. Current image generation techniques applied to fundus photography encounter difficulties in producing high-resolution retinal images, particularly in capturing minute vascular lesions. To address these issues, we introduce a novel conditional generative adversarial network (UWAFA-GAN) to synthesize UWF-FA from UWF-SLO. This approach employs multi-scale generators and an attention transmit module to efficiently extract both global structures and local lesions. Additionally, to counteract the image blurriness issue that arises from training with misaligned data, a registration module is integrated within this framework. Our method performs non-trivially on inception scores and details generation. Clinical user studies further indicate that the UWF-FA images generated by UWAFA-GAN are clinically comparable to authentic images in terms of diagnostic reliability. Empirical evaluations on our proprietary UWF image datasets elucidate that UWAFA-GAN outperforms extant methodologies.

Current deep models excel in object detection for classification and localization. However, precise object rotation estimation within the visual context of an input image remains underexplored due to the lack of object datasets with rotation annotations. This paper addresses these challenges by tackling rotation estimation for parked bikes with respect to their parking area. Firstly, 3D graphics were leveraged to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Subsequently, an object-to-spot rotation estimator (OSRE) is introduced by extending object detection to regress bike rotations in two axes. As the proposed model trained purely on synthetic data, image smoothing techniques adopted during deployment on real-world images. The proposed OSRE has undergone evaluation on both synthetic and real-world data, showing promising results. Our data and code are available at https://saghiralfasly.github.io/OSRE-Project/.

Intracerebral Hemorrhage (ICH) is a severe condition resulting from damaged brain blood vessel ruptures, often leading to complications and fatalities. Timely and accurate prognosis and management are essential due to its high mortality rate. However, conventional methods heavily rely on subjective clinician expertise, which can lead to inaccurate diagnoses and delays in treatment. Artificial intelligence (AI) models have been explored to assist clinicians, but many prior studies focused on model modification without considering domain knowledge. This paper introduces a novel deep learning algorithm, GCS-ICHNet, which integrates multimodal brain CT image data and the Glasgow Coma Scale (GCS) score to improve ICH prognosis. The algorithm utilizes a transformer-based fusion module for assessment. GCS-ICHNet demonstrates high sensitivity 81.03% and specificity 91.59%, outperforming average clinicians and other state-of-the-art methods. The code is available at https://github.com/Windbelll/Prognosis-analysis-of-cerebral-hemorrhage.

Parkinson’s disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient’s motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the${l}_{\mathrm {2,p}}$norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson’s Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.

The capture of long-distance dependencies presents an efficient approach for feature learning and extraction. Especially the Transformers models that explore the dependencies within a long sequence, have swept the fields of natural language processing with their powerful performance. However, Transformers require extremely high computing power due to the huge amount of parameters, and it cannot achieve parallelism since it outputs tokens one by one. In this work, inspired by Transformers, we propose a self-attention encoder module (SAEM) that focuses on learning the connections between each position and all other positions in the image, which preserves the efficiency of Transformers but with less calculation and faster inference speed. In our SAEM, different group of internal feature maps within images captured by multiple scaled self-attentions are cascaded to generate global context information. Based on our SAEM, a lightweight and parallel network is designed for segmentation of intracranial blood vessels. Moreover, a data augmentation method is proposed, called sliced mosaic permutation, which makes the original image features richer and alleviates the problem of category imbalance, via cutting the original images with different scales and recombining randomly. We apply SAEM and sliced mosaic permutation to the task of intracranial blood vessel segmentation, the result shows that our method outperforms competitive methods in both visualization results and quantitative evaluation.

Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemical properties of protein (e.g., hydrophobicity and hydrophilicity). Deep polynomial network (DPN) is well-suited to integrate these modalities since it can represent any function on a finite sample dataset via the supervised deep learning algorithm. We propose a multimodal DPN (MDPN) algorithm to effectively integrate these modalities to enhance prediction performance. MDPN consists of a two-stage DPN, the first stage feeds multiple protein features into DPN encoding to obtain high-level feature representation while the second stage fuses and learns features by cascading three types of high-level features in the DPN encoding. We employ a regularized extreme learning machine to predict PPIs. The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97.87%, 99.90%, and 98.11%, respectively. The proposed method also achieves good accuracies on other datasets. Furthermore, we test our method on three kinds of PPI networks and obtain superior prediction results.