Weakly Supervised Classification
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Benchmarks
THYME-2016
Most implemented
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Kernel Density Matrices for Probabilistic Deep Learning
Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Papers
Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scenarios, candidate labels are often influen…
Weakly Supervised ClassificationPartial Label LearningDSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification
Whole-slide images (WSIs) are critical for cancer diagnosis due to their ultra-high resolution and rich semantic content. However, their massive size and the limited availability of fine-grained annotations pose substant…
image-classificationImage ClassificationWeakly Supervised Classificationwhole slide imagesA Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Multiple instance learning (MIL) is a promising approach for weakly supervised classification in pathology using whole slide images (WSIs). However, conventional MIL methods such as Attention-Based Deep Multiple Instance…
Computational EfficiencyMultiple Instance LearningWeakly Supervised Classificationwhole slide imagesMSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
Multiple instance learning (MIL) has become a standard paradigm for weakly supervised classification of whole slide images (WSI). However, this paradigm relies on the use of a large number of labelled WSIs for training. …
image-classificationImage ClassificationLanguage ModellingLarge Language Model+3Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology
Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations performed by clinicians, which are scarc…
ClassificationSelf-Supervised LearningWeakly Supervised ClassificationRoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classification
Whole slide image (WSI) classification is a critical task in computational pathology. However, the gigapixel-size of such images remains a major challenge for the current state of deep-learning. Current methods rely on m…
image-classificationImage ClassificationMultiple Instance LearningPosition+1