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 351–400 of 744 papers

TitleStatusHype
SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis—0
Sparse Multi-Modal Graph Transformer With Shared-Context Processing for Representation Learning of Giga-Pixel Images—0
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning—0
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection—0
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes—0
Spatio-Temporal Action Localization in a Weakly Supervised Setting—0
Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy—0
Spatio-Temporal Instance Learning: Action Tubes from Class Supervision—0
Spot On: Action Localization from Pointly-Supervised Proposals—0
Studying The Effect of MIL Pooling Filters on MIL Tasks—0
Support Vector Machines for Multiple-Instance Learning—0
Task-oriented Embedding Counts: Heuristic Clustering-driven Feature Fine-tuning for Whole Slide Image Classification—0
Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformer—0
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology—0
The Whole Pathological Slide Classification via Weakly Supervised Learning—0
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images—0
Tiny Object Detection with Single Point Supervision—0
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation—0
Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images—0
Towards end-to-end disease prediction from raw metagenomic data—0
Towards Hierarchical Regional Transformer-based Multiple Instance Learning—0
Towards Open Set Video Anomaly Detection—0
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation—0
Towards Universal Representation for Unseen Action Recognition—0
Training image classifiers using Semi-Weak Label Data—0
Training Medical Image Analysis Systems like Radiologists—0
Training object class detectors with click supervision—0
Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network—0
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection—0
Two-person interaction detection using body-pose features and multiple instance learning—0
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms—0
Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data—0
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography—0
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos—0
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification—0
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection—0
User Personalized Satisfaction Prediction via Multiple Instance Deep Learning—0
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning—0
Using Multiple Instance Learning to Build Multimodal Representations—0
Utilizing the Instability in Weakly Supervised Object Detection—0
Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions—0
Video Representation Learning Using Discriminative Pooling—0
Video Segmentation via Multiple Granularity Analysis—0
VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction—0
W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection—0
Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision—0
Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling—0
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network—0
Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification—0
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health—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