AIMC Topic: Machine Learning

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Completed Feature Disentanglement Learning for Multimodal MRIs Analysis.

IEEE journal of biomedical and health informatics
Multimodal MRIs play a crucial role in clinical diagnosis and treatment. Feature disentanglement (FD)-based methods, aiming at learning superior feature representations for multimodal data analysis, have achieved significant success in multimodal lea...

Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

IEEE journal of biomedical and health informatics
Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for ...

Enhancing Medical Vision-Language Contrastive Learning via Inter-Matching Relation Modeling.

IEEE transactions on medical imaging
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred ...

STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication opera...

AGCLNDA: Enhancing the Prediction of ncRNA-Drug Resistance Association Using Adaptive Graph Contrastive Learning.

IEEE journal of biomedical and health informatics
Non-coding RNAs (ncRNAs), which do not encode proteins, have been implicated in chemotherapy resistance in cancer treatment. Given the high costs and time requirements of traditional biological experiments, there is an increasing need for computation...

Hierarchical Multi-Class Group Correlation Learning Network for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Hierarchical approaches have been tremendously successful at multi-label segmentation. However, it has been shown they may seriously suffer from the problem of only imposing constraints on shallow layers while ignoring deep relationships in the label...

Automated Ensemble Multimodal Machine Learning for Healthcare.

IEEE journal of biomedical and health informatics
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. Thi...

Learning Sensor Sample-Reweighting for Dynamic Early-Exit Activity Recognition Via Meta Learning.

IEEE journal of biomedical and health informatics
During recent years, dynamic early-exit has provided a promising paradigm to improve the computational efficiency of deep neural networks by constructing multiple classifiers to let easy samples exit at shallow layers while avoiding redundant computa...

CAPI-Detect: machine learning in capillaroscopy reveals new variables influencing diagnosis.

Rheumatology (Oxford, England)
OBJECTIVES: Nailfold videocapillaroscopy (NVC) is the gold standard for diagnosing SSc and differentiating primary from secondary RP. The CAPI-Score algorithm, designed for simplicity, classifies capillaroscopy scleroderma patterns (CSPs) using a lim...

Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand...