EPICPred: Predicting Phenotypes Driven by Epitope-Binding TCRs Using Attention-Based Multiple Instance Learning

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Bioengineering, Immunology,
epitope-binding TCRs   multiple instance learning   phenotype prediction   cancer   COVID-19  

T-cell receptors (TCRs) play a crucial role in the adaptive immune system by recognizing pathogens through binding to specific antigen epitopes. Understanding the interactions between TCRs and epitopes is essential for uncovering the biological mechanisms of immune responses and developing T cell-mediated immunotherapies. However, although the impo...

AugDiff: Diffusion-Based Feature Augmentation for Multiple Instance Learning in Whole Slide Image

Information Science, Computer Science, Artificial Intelligence, Medicine, Oncology,
feature augmentation   diffusion model   multiple instance learning   whole slide image   computational pathology   deep learning   image generation  

Diffusion-Based Feature Augmentation: A Novel Approach for Multiple Instance Learning in Whole Slide Images Academic Background and Research Motivation In computational pathology, effectively analyzing Whole Slide Images (WSIs) is a burgeoning area of research. WSIs are ultra-high-resolution images with a broad field of view and are widely employed...

Graph Neural Network for Representation Learning of Lung Cancer

Graph Neural Network for Representation Learning of Lung Cancer

Information Science, Computer Science, Artificial Intelligence, Medicine, Respiratory System, Medical Imaging, Oncology,
multiple instance learning   graph   whole slide images   graph neural networks   lung cancer   variational autoencoder  

Representation Learning of Lung Cancer Based on Graph Neural Networks Background Introduction With the rapid development of digital pathology, image-based diagnostic systems are becoming increasingly important in precise pathological diagnosis. These systems rely on Multiple Instance Learning (MIL) technology for Whole Slide Images (WSIs). However,...

Hyperbolic secant representation of the logistic function: Application to probabilistic multiple instance learning for CT intracranial hemorrhage detection

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
multiple instance learning   gaussian processes   bayesian variational inference   intracranial hemorrhage detection  

There has long been a problem of “weak supervision” in the field of artificial intelligence, where only part of the labels are observable in the training data, while the remaining labels are unknown. Multiple Instance Learning (MIL) is a paradigm to address this issue. In MIL, the training data is divided into multiple “bags”, each containing multi...