Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self …
Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
Chemical complexity challenge: Is multi‐instance machine learning a solution?
Molecules are complex dynamic objects that can exist in different molecular forms
(conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known …
(conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known …
Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …
very high resolutions and usually lack localized annotations. WSI classification can be cast …
Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution
Multiple instance learning (MIL) is a typical weakly-supervised learning method where the
label is associated with a bag of instances instead of a single instance. Despite extensive …
label is associated with a bag of instances instead of a single instance. Despite extensive …
DT-MIL: deformable transformer for multi-instance learning on histopathological image
Learning informative representations is crucial for classification and prediction tasks on
histopathological images. Due to the huge image size, whole-slide histopathological image …
histopathological images. Due to the huge image size, whole-slide histopathological image …
Modern hopfield networks and attention for immune repertoire classification
A central mechanism in machine learning is to identify, store, and recognize patterns. How to
learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent …
learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent …
Boosting whole slide image classification from the perspectives of distribution, correlation and magnification
Bag-based multiple instance learning (MIL) methods have become the mainstream for
Whole Slide Image (WSI) classification. However, there are still three important issues that …
Whole Slide Image (WSI) classification. However, there are still three important issues that …
3D-GLCM CNN: A 3-dimensional gray-level co-occurrence matrix-based CNN model for polyp classification via CT colonography
Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a
significant clinical impact on early detection and identifying optimal treatment of colorectal …
significant clinical impact on early detection and identifying optimal treatment of colorectal …
Rethinking multiple instance learning for whole slide image classification: A good instance classifier is all you need
Weakly supervised whole slide image classification is usually formulated as a multiple
instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut …
instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut …
Setmil: spatial encoding transformer-based multiple instance learning for pathological image analysis
Considering the huge size of the gigapixel whole slide image (WSI), multiple instance
learning (MIL) is normally employed to address pathological image analysis tasks, where …
learning (MIL) is normally employed to address pathological image analysis tasks, where …