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
Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance—0
Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning—0
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition—0
Detection of Major ASL Sign Types in Continuous Signing For ASL Recognition—0
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning—0
Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images—0
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images—0
Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning—0
Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection—0
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning—0
Discriminatively Trained Latent Ordinal Model for Video Classification—0
Discriminative Video Representation Learning Using Support Vector Classifiers—0
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network—0
Dissimilarity-based Ensembles for Multiple Instance Learning—0
Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism—0
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation—0
Distribution Based MIL Pooling Filters are Superior to Point Estimate Based Counterparts—0
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification—0
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images—0
Dual Graph Attention based Disentanglement Multiple Instance Learning for Brain Age Estimation—0
Dynamic Hypergraph Representation for Bone Metastasis Cancer Analysis—0
EEG-Language Modeling for Pathology Detection—0
Effective and Interpretable Information Aggregation with Capacity Networks—0
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data—0
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images—0
Ensemble of Part Detectors for Simultaneous Classification and Localization—0
Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning—0
Estimating Target Signatures with Diverse Density—0
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification—0
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning—0
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis—0
Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma—0
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning—0
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification—0
Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detection—0
Feature and Region Selection for Visual Learning—0
Few-shot Anomaly Detection in Text with Deviation Learning—0
Few-shot Weakly-Supervised Object Detection via Directional Statistics—0
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos—0
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers—0
Multiple Instance Learning by Discriminative Training of Markov Networks—0
Multiple--Instance Learning: Christoffel Function Approach to Distribution Regression Problem—0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition—0
Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data—0
Multiple Instance Learning for Cheating Detection and Localization in Online Examinations—0
Multiple Instance Learning for Content Feedback Localization without Annotation—0
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets—0
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential—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