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 401–450 of 744 papers

TitleStatusHype
End-to-end Multiple Instance Learning with Gradient AccumulationCode1
Polar Transformation Based Multiple Instance Learning Assisting Weakly Supervised Image Segmentation With Loose Bounding Box AnnotationsCode0
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution GeneralizationCode1
Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups—0
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural NetworksCode1
ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image ClassificationCode1
Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learningCode0
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer—0
Model Agnostic Interpretability for Multiple Instance LearningCode1
A Proposal-Based Paradigm for Self-Supervised Sound Source Localization in Videos—0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation—0
Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and ClassificationCode0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data—0
Deciphering antibody affinity maturation with language models and weakly supervised learningCode1
Multi-Attention Multiple Instance Learning—0
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy SlidesCode1
Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural NetworkCode0
Explainable multiple abnormality classification of chest CT volumesCode0
Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data—0
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning—0
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging—0
Nested Multiple Instance Learning with Attention MechanismsCode0
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images—0
Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection—0
Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection—0
Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding—0
Bounding Box Tightness Prior for Weakly Supervised Image SegmentationCode1
CDRNet: Accurate Cup-to-Disc Ratio Measurement with Tight Bounding Box Supervision in Fundus Photography Using Deep LearningCode0
Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification—0
Active Deep Multiple Instance Learning—0
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide ImagesCode1
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism DetectionCode1
WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need—0
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study—0
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image ClassificationCode0
Joint Multiple Intent Detection and Slot Filling via Self-distillation—0
Foreground-Action Consistency Network for Weakly Supervised Temporal Action LocalizationCode1
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation—0
Adversarial learning of cancer tissue representationsCode1
Explainable Deep Few-shot Anomaly Detection with Deviation NetworksCode1
A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images—0
End-to-end Multiple Instance Learning for Whole-Slide Cytopathology of Urothelial CarcinomaCode1
Attention-based Multiple Instance Learning with Mixed Supervision on the Camelyon16 Dataset—0
Detecting genetic alterations in BRAF and NTRK as oncogenic drivers in digital pathology images: towards model generalization within and across multiple thyroid cohorts.—0
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations—0
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancerCode0
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma ClassificationCode0
Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning—0
Weakly Supervised Temporal Adjacent Network for Language GroundingCode1
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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