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
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning—0
Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification—0
Using Multiple Instance Learning for Explainable Solar Flare PredictionCode0
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly DetectionCode0
Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning—0
Object Localization under Single Coarse Point Supervision—0
Attention-effective multiple instance learning on weakly stem cell colony segmentation—0
Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling—0
Polar Transformation Based Multiple Instance Learning Assisting Weakly Supervised Image Segmentation With Loose Bounding Box AnnotationsCode0
Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups—0
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
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
Multi-Attention Multiple Instance Learning—0
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
Nested Multiple Instance Learning with Attention MechanismsCode0
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
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
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
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
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation—0
A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images—0
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations—0
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
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
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation—0
Explaining decision of model from its predictionCode0
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction—0
Learning to Select Cuts for Efficient Mixed-Integer Programming—0
DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learningCode0
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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