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 151–200 of 744 papers

TitleStatusHype
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation ExtractionCode1
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive LearningCode1
MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity ConstraintCode1
Multi-head Attention-based Deep Multiple Instance LearningCode1
Multi-level Multiple Instance Learning with Transformer for Whole Slide Image ClassificationCode1
Attention-Challenging Multiple Instance Learning for Whole Slide Image ClassificationCode1
Multiple Anchor Learning for Visual Object DetectionCode1
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly DetectionCode1
Multiple Instance Learning with Mixed Supervision in Gleason GradingCode1
Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology ImagesCode1
Data Efficient and Weakly Supervised Computational Pathology on Whole Slide ImagesCode1
Characterizing multiple instance datasets—0
Certainty Pooling for Multiple Instance Learning—0
A Proposal-Based Paradigm for Self-Supervised Sound Source Localization in Videos—0
Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
Cascade Attentive Dropout for Weakly Supervised Object Detection—0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
Anomaly Detection with Inexact Labels—0
CanvOI, an Oncology Intelligence Foundation Model: Scaling FLOPS Differently—0
Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and L_1 Regularization—0
AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients—0
An attention-based multi-resolution model for prostate whole slide imageclassification and localization—0
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property—0
Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures—0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning—0
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer—0
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images—0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation—0
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
In Defense of LSTMs for Addressing Multiple Instance Learning Problems—0
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis—0
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images—0
Boosting Point-Supervised Temporal Action Localization through Integrating Query Reformation and Optimal Transport—0
A Feature Selection Method for Multivariate Performance Measures—0
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models—0
An In-field Automatic Wheat Disease Diagnosis System—0
Estimating Target Signatures with Diverse Density—0
A new Time-decay Radiomics Integrated Network (TRINet) for short-term breast cancer risk prediction—0
Active Deep Multiple Instance Learning—0
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling—0
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective—0
Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning—0
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification—0
Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification—0
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models—0
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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