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 301–350 of 744 papers

TitleStatusHype
Multiple Instance Learning with Trainable Decision Tree Ensembles—0
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime—0
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance LearningCode0
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography—0
Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance—0
Weakly Supervised Image Segmentation Beyond Tight Bounding Box AnnotationsCode0
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide imagesCode0
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification—0
Boosting Positive Segments for Weakly-Supervised Audio-Visual Video ParsingCode0
LNPL-MIL: Learning from Noisy Pseudo Labels for Promoting Multiple Instance Learning in Whole Slide Image—0
Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence RefinementCode0
CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation—0
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms—0
Weak-Shot Object Detection Through Mutual Knowledge Transfer—0
Sparse Multi-Modal Graph Transformer With Shared-Context Processing for Representation Learning of Giga-Pixel Images—0
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
Learning to Detect Semantic Boundaries with Image-level Class Labels—0
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue MicroarraysCode0
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Using Multiple Instance Learning to Build Multimodal Representations—0
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
Giga-SSL: Self-Supervised Learning for Gigapixel ImagesCode1
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement LearningCode1
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
Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover ClassificationCode1
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
Iterative Patch Selection for High-Resolution Image RecognitionCode1
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive LearningCode1
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Overlooked Video Classification in Weakly Supervised Video Anomaly DetectionCode1
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation—0
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image ClassificationCode1
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets—0
Real-world Video Anomaly Detection by Extracting Salient Features in Videos—0
Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology ImagesCode1
Partially Relevant Video RetrievalCode1
All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classificationCode0
Towards Open Set Video Anomaly Detection—0
Multiple Instance Neuroimage TransformerCode1
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
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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