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 501–550 of 744 papers

TitleStatusHype
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
A Sample-Based Training Method for Distantly Supervised Relation Extraction with Pre-Trained Transformers—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
Cross-Modal learning for Audio-Visual Video ParsingCode0
Few-shot Weakly-Supervised Object Detection via Directional Statistics—0
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
Towards end-to-end disease prediction from raw metagenomic data—0
Distribution Based MIL Pooling Filters are Superior to Point Estimate Based Counterparts—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
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
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest RadiographsCode0
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations—0
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning—0
Certainty Pooling for Multiple Instance Learning—0
Deep Domain Adaptation for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labelled VideosCode0
Sharp Multiple Instance Learning for DeepFake Video Detection—0
Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM—0
Attention based Multiple Instance Learning for Classification of Blood Cell DisordersCode0
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos—0
Learning Person Re-identification Models from Videos with Weak Supervision—0
Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-DecodingCode0
MSA-MIL: A deep residual multiple instance learning model based on multi-scale annotation for classification and visualization of glomerular spikes—0
Multiple Instance Learning for Content Feedback Localization without Annotation—0
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection—0
Pain Intensity Estimation from Mobile Video Using 2D and 3D Facial Keypoints—0
Studying The Effect of MIL Pooling Filters on MIL Tasks—0
Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution—0
Theory and Algorithms for Shapelet-based Multiple-Instance LearningCode0
Kernel Self-Attention in Deep Multiple Instance Learning—0
Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning—0
Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning—0
Learning from Noisy Labels with Noise Modeling Network—0
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms—0
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers—0
Deep Learning based detection of Acute Aortic Syndrome in contrast CT images—0
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance LearningCode0
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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