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 451–500 of 744 papers

TitleStatusHype
Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images—0
Real-world Video Anomaly Detection by Extracting Salient Features in Videos—0
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model—0
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers—0
Relaxed Multiple-Instance SVM with Application to Object Discovery—0
Reliable counting of weakly labeled concepts by a single spiking neuron model—0
An attention-based multi-resolution model for prostate whole slide imageclassification and localization—0
Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests—0
Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training—0
RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification—0
Revisiting Multiple Instance Neural Networks—0
Robust compressive tracking via online weighted multiple instance learning—0
Robust sensitivity control in digital pathology via tile score distribution matching—0
Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles—0
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment—0
Scaling up sign spotting through sign language dictionaries—0
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology—0
Self-Classification Enhancement and Correction for Weakly Supervised Object Detection—0
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation—0
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime—0
Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification—0
Semantic Component Analysis—0
Semantics-Aware Attention Guidance for Diagnosing Whole Slide Images—0
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding—0
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis—0
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences—0
Set-Constrained Viterbi for Set-Supervised Action Segmentation—0
Sharp Multiple Instance Learning for DeepFake Video Detection—0
Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding—0
Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning—0
Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis—0
Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification—0
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection—0
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
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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