SOTAVerified

Multiple Instance Learning

Multiple Instance Learning is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\{x_1,x_2, \ldots,x_M\}$, and there is one single label $Y$ per bag, $Y\in\{0, 1\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. On the other hand, a bag is positive, if at least one instance in the bag is positive.

Source: Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

Papers

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Showing 451–500 of 744 papers

TitleStatusHype
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation—0
Explaining decision of model from its predictionCode0
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image ClassificationCode1
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction—0
Learning to Select Cuts for Efficient Mixed-Integer Programming—0
3D Spatial Recognition without Spatially Labeled 3DCode1
DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learningCode0
SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image ClassificationCode1
mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning TechniquesCode0
Weakly supervised deep learning-based intracranial hemorrhage localizationCode0
Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics—0
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI—0
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative LearningCode1
A Sample-Based Training Method for Distantly Supervised Relation Extraction with Pre-Trained Transformers—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
Fast Hierarchical Games for Image ExplanationsCode1
Multiple instance active learning for object detectionCode1
Cross-Modal learning for Audio-Visual Video ParsingCode0
Face Forensics in the WildCode1
Few-shot Weakly-Supervised Object Detection via Directional Statistics—0
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
Training image classifiers using Semi-Weak Label Data—0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI—0
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events—0
An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures—0
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude LearningCode1
A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action LocalizationCode1
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide ImagesCode1
Distribution Based MIL Pooling Filters are Superior to Point Estimate Based Counterparts—0
Towards end-to-end disease prediction from raw metagenomic data—0
A Visual Mining Approach to Improved Multiple-Instance Learning—0
Nonlinear Distribution Regression for Remote Sensing Applications—0
Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology—0
PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data—0
Cascade Attentive Dropout for Weakly Supervised Object Detection—0
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive LearningCode1
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning—0
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification—0
Power pooling: An adaptive pooling function for weakly labelled sound event detection—0
Watch, read and lookup: learning to spot signs from multiple supervisorsCode1
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest RadiographsCode0
Multiple Instance Learning with Center Embeddings for Histopathology ClassificationCode1
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations—0
Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning NetworksCode1
Federated Learning for Computational Pathology on Gigapixel Whole Slide ImagesCode1
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance SegmentationCode1
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning—0
Certainty Pooling for Multiple Instance Learning—0
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All datasetsCAMELYON16TCGAElephantMusk v1Musk v2

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Snuffy (DINO Exhaustive)AUC0.99—Unverified
2Snuffy (SimCLR Exhaustive)AUC0.97—Unverified
3CAMILAUC0.96—Unverified
4CAMIL (CAMIL-L)AUC0.95—Unverified
5CAMIL (CAMIL-G)AUC0.95—Unverified
6DTFD-MIL (AFS)AUC0.95—Unverified
7DTFD-MIL (MAS)AUC0.95—Unverified
8DTFD-MIL (MaxMinS)AUC0.94—Unverified
9TransMILAUC0.93—Unverified
10DSMIL-LCAUC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DTFD-MIL (MAS)AUC0.96—Unverified
2DTFD-MIL (AFS)ACC0.95—Unverified
3Snuffy (SimCLR Exhaustive)ACC0.95—Unverified
4DSMIL-LCACC0.93—Unverified
5DSMILACC0.92—Unverified
6DTFD-MIL (MaxMinS)ACC0.89—Unverified
7TransMILACC0.88—Unverified
8DTFD-MIL (MaxS)ACC0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyAUC0.97—Unverified
2DSMILACC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyACC0.96—Unverified
2DSMILACC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1DSMILACC0.93—Unverified
2SnuffyACC0.79—Unverified