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 401–450 of 744 papers

TitleStatusHype
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification—0
Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence RefinementCode0
Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology ImagesCode0
Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images—0
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue MicroarraysCode0
Learning to Detect Semantic Boundaries with Image-level Class Labels—0
Using Multiple Instance Learning to Build Multimodal Representations—0
Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial PerturbationsCode0
Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and GenomicsCode0
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CTCode0
Language models are good pathologists: using attention-based sequence reduction and text-pretrained transformers for efficient WSI classificationCode0
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer DiagnosisCode0
An Aggregation of Aggregation Methods in Computational Pathology—0
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images—0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation—0
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets—0
Real-world Video Anomaly Detection by Extracting Salient Features in Videos—0
All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classificationCode0
Towards Open Set Video Anomaly Detection—0
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection—0
Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences—0
Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification—0
Isoform Function Prediction Using a Deep Neural Network—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI ApproachCode0
Is Attention Interpretation? A Quantitative Assessment On Sets—0
Effective and Interpretable Information Aggregation with Capacity Networks—0
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment—0
Anomaly-aware multiple instance learning for rare anemia disorder classificationCode0
Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News—0
Weakly-Supervised Temporal Action Localization by Progressive Complementary LearningCode0
Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images—0
Balancing Bias and Variance for Active Weakly Supervised LearningCode0
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential—0
Pancreatic Cancer ROSE Image Classification Based on Multiple Instance Learning with Shuffle Instances—0
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology—0
Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations—0
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations—0
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance LearningCode0
Attention Awareness Multiple Instance Neural Network—0
Learning Instance Representation Banks for Aerial Scene Classification—0
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition—0
Scaling up sign spotting through sign language dictionaries—0
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene ClassificationCode0
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images—0
Colorectal cancer survival prediction using deep distribution based multiple-instance learning—0
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification—0
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information—0
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection—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