As multimedia advances, there is a growing need for machines to adeptly understand diverse social relations . Traditional methods for recognizing these relations, which are limited to a fixed number of classes, are ill-equipped for continual learning as new social interactions emerge. To address this prob-lem, we propose a pioneering Graph-based Interactive Knowledge Distillation (GI-KD) method for social relation continual learning. GI-KD, embedded in a class incremental learning structure, creates a balanced system where previously learned social relations and new knowledge are positioned at either end of the scale. The old and new knowledge is learned dynamically by adjusting the tilt of the balance. To achieve this balance, we propose a novel Libra loss function, which evaluate the relative contribution of old and new information and thus guides the adaptive fine-tuning of the model. We evaluate the GI-KD on three public social relation recognition (SRR) datasets, under different data distribution strategies. Our method shows a remarkable average 3.6% increase in incremental accuracy over current CIL techniques, effectively reducing catastrophic forgetting. Furthermore, GI-KD improves mAP and Acc by 4.6%, 5.4%, and 4.5%, respectively, compared to current CIL techniques, highlighting its strength in both continual learning and SRR.

MOTIVATION: Tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC), which has a strain- or lineage-based clonal population structure. The evolution of drug-resistance in the MTBC poses a threat to successful treatment and eradication of TB. Machine learning approaches are being increasingly adopted to predict drug-resistance and characterize underlying mutations from whole genome sequences. However, such approaches may not generalize well in clinical practice due to confounding from the population structure of the MTBC. RESULTS: To investigate how population structure affects machine learning prediction, we compared three different approaches to reduce lineage dependency in random forest (RF) models, including stratification, feature selection, and feature weighted models. All RF models achieved moderate-high performance (area under the ROC curve range: 0.60-0.98). First-line drugs had higher performance than second-line drugs, but it varied depending on the lineages in the training dataset. Lineage-specific models generally had higher sensitivity than global models which may be underpinned by strain-specific drug-resistance mutations or sampling effects. The application of feature weights and feature selection approaches reduced lineage dependency in the model and had comparable performance to unweighted RF models. AVAILABILITY AND IMPLEMENTATION: https://github.com/NinaMercedes/RF_lineages.

Abstract Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain.

Identifying social relationships from images is a challenging yet promising research area with great potential for improving human health and enhancing our understanding of social networks. However, present endeavors in this field tend to concentrate on leveraging visual features for the exploration of social relationships, while disregarding certain concealed information that lies beneath these features, such as interpersonal similarity. These methodologies may result in inadequate visual data encoding, thereby imposing limitations on the accuracy of social relationship recognition. In light of this, we propose a novel framework that utilizes interpersonal similarities within images to provide more information for identifying social relationships, thereby mitigating the issue of insufficient feature exploration. Furthermore, our proposed framework incorporates an innova-tive CF-Loss function that effectively incentivizes the identifica-tion of accurate social relationships while penalizing incorrect identifications, ultimately bolstering the model's capacity to dis-criminate between distinct social relationships. Our experimental findings demonstrate the superiority of our proposed framework over state-of-the-art methods on public datasets, confirming its effectiveness and accuracy in identifying social relationships.

Iron ore feed-load control is one of the most critical settings in a mineral grinding process. It has direct impact on the quality of final iron products. The setting of the feed load heavily replies the characteristics of the ore pellets. However, such characteristics are challenging to acquire in many production environments, requiring speical equipments and complicated modelling process with a high cost. To provide an low-cost and easier-to-implement solution, in this paper, we present our work on using deep learning models for direct ore feed load estimation from ore pellet images. To address the challenges caused by the large size of ore images and the shortage of accurately annotated data, we proposed to use a weakly supervised learning apporach with a two-stage model training algorithm and two neural network architectures developed. The experiment results show competitive model performance, and the trained models can be used for real-time feed load estimation for grind process optimisation.

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.

This paper presents a big data analystics platform for clinical research and practice in the Gastroenterology Department of Xiangya Hospital at Central South University in China. This platform features a comprehensive and systematic support of big data in digestive medicine including geneneral health management, clinical gastroenterology practice, and related genomics research, which is proven to be helpful in real world clinical practices. A typical use case of integrated analysis based on electronic medical records and colonoscopy data was presented and discussed, the analaystic report on risk factors of colorectal diseases shows a reasonable recommendation about the age when people should start to screen the colorectal cancer, which could be very useful to individual and group health management for the general population in China.

Specials medications are personalized formulations manufactured on demand for patients with unique prescription requirements and constitute an essential component of patient treatment. Specials are becoming increasingly in demand due to the need for personalized and precision medicine. The timely provision of optimal personalized medicine, however, is challenging, subject to strict regulatory processes, and is expert intensive. In this paper, we propose a new medical formulation engine (MFE) that performs semantic search across multiple disparate formulations archives to enable data driven formulation intelligence. We develop a new platform for medical formulations recognition (MFR) that curates a new dataset comprising formulations and non-formulations (clinical) text and uses a novel pipeline encompassing deep feature extraction and one-class support vector machine learning. The proposed MFR framework demonstrates promising performance and can be used as a benchmark for future research in formulations recognition.

Aortic stent has been widely used in restoring vascular stenosis and assisting patients with cardiovascular disease. The effective visualization of aortic stent is considered to be critical to ensure the effectiveness and functions of the aortic stent in clinical practice. Volume rendering with ray casting has been used as an effective approach to enable the effective visualization of aortic stent. The volume rendering relies on the transfer function that converts the medical images into optical attributes including color and transparency. This article proposes a new transfer function, namely, the multi-dimensional transfer function, to provide additional transparency value of a voxel. The proposed approach using the additional transparency value effectively assists the distinguishing of tissues that have the same CT value. The transparency values are simultaneously determined by gray threshold and gray change threshold, which can recognize the unnecessary structures such as bones transparent. A series of experimental results demonstrate that the situation of aorta stent of a patient can be directly observed, and the angle of view can be switched arbitrarily. The proposed method provides a new way for the operation of a virtual endoscopy to reach the place of blood vessels that a traditional endoscopy fails to reach.

Symptoms and signs (symptoms in brief) are the essential clinical manifestations for traditional Chinese medicine (TCM) diagnosis and treatments. To gain insights into the molecular mechanism of symptoms, this paper presents a network-based data mining method to integrate multiple phenotype-genotype data sources and predict the prioritizing gene rank list of symptoms. The result of this pilot study suggested some insights on the molecular mechanism of symptoms.

This paper proposes a new template model for the simulation of casting defects which are classified, according to shape, into three main types: the single defect with circular or elliptical shape, shrinkage defects with stochastic discontinuities, and the cavity or sponge shrinkage defects. For effective simulation, different nesting stencil plates are designed to reflect the characteristics of different casting defects. These include intensity, orientation, size, and shape. The proposed approach also uses geometric diffusion to demonstrate the production of simulated defects with effective shading and contrast when compared to their background. In order to evaluate the effectiveness of the proposed approach, the simulated casting defects are superposed on real radioscopic images of casting pieces and compared with real defects by extensive visual inspection. On the other hand, in order to verify the similarity of the simulated defects and the real defects, we have used our defect inspection algorithm to recognize both real and simulated defects in the same image. The experimental results show that the proposed defect simulation approach can produce a large range of simulated casting defects, which can be utilized as sample images to tune the parameters of casting inspection algorithms.

Many features have been proposed for the detection of microcalcification clusters (MCCs) or classification of benign/malignant MCCs. However, most of them were designed based on the characteristics of MCC. In this paper, 16 features, which have been commonly adopted in many applications, are examined and six new features based on the linear structure are proposed. To evaluate the effectiveness of these six features, 800 suspicious regions detected from 320 full-field mammograms are equally divided into two parts for training and testing respectively. Experiments demonstrate that the area under the receiver operating characteristic (ROC) is increased from 0.86 to 0.89 after the new features are added into the set of feature selection. In the best feature sequence selected by the sequential floating forward search (SFFS) algorithm, the new proposed features take up the half number of features in the sequence.

Summary form only given. Constructing fast, accurate classifiers for large data sets is an important task in data mining and knowledge discovery. In this research paper, a new classification method called multi-class classification based on association rules (MCAR) is presented. MCAR uses an efficient technique for discovering frequent items and employs a rule ranking method which ensures detailed rules with high confidence are part of the classifier. After experimentation with fifteen different data sets, the results indicated that the proposed method is an accurate and efficient classification technique. Furthermore, the classifiers produced are highly competitive with regards to error rate and efficiency, if compared with those generated by popular methods like decision trees, RIPPER and CBA.

Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining together can produce more efficient and accurate classifiers than traditional classification techniques. In this paper, the problem of producing rules with multiple labels is investigated. We propose a new associative classification approach called multi-class, multi-label associative classification (MMAC). This paper also presents three measures for evaluating the accuracy of data mining classification approaches to a wide range of traditional and multi-label classification problems. Results for 28 different datasets show that the MMAC approach is an accurate and effective classification technique, highly competitive and scalable in comparison with other classification approaches.