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

Showing 501–550 of 744 papers

TitleStatusHype
Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach—0
Classification of COPD with Multiple Instance Learning—0
Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features—0
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning—0
Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology—0
Classroom Video Assessment and Retrieval via Multiple Instance Learning—0
LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening—0
C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection—0
Colorectal cancer survival prediction using deep distribution based multiple-instance learning—0
Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis—0
Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans—0
Confidence-Rated Multiple Instance Boosting for Object Detection—0
Constrained Deep Weak Supervision for Histopathology Image Segmentation—0
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking—0
Contrastive Multiple Instance Learning for Weakly Supervised Person ReID—0
Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection—0
Convex Multiple-Instance Learning by Estimating Likelihood Ratio—0
CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation—0
Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology—0
Cross-Modal Retrieval with Implicit Concept Association—0
CytoFM: The first cytology foundation model—0
Data efficient deep learning for medical image analysis: A survey—0
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images—0
Deep Learning based detection of Acute Aortic Syndrome in contrast CT images—0
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology—0
Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs—0
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma—0
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks—0
Deep Multiple Instance Feature Learning via Variational Autoencoder—0
Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News—0
Deep Multiple Instance Learning for Airplane Detection in High Resolution Imagery—0
Deep Multiple Instance Learning for Image Classification and Auto-Annotation—0
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets—0
Deep Multiple Instance Learning with Distance-Aware Self-Attention—0
Deep Multiple Instance Learning with Gaussian Weighting—0
Deep Weakly-Supervised Domain Adaptation for Pain Localization in Videos—0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance—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
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
Show:102550
← PrevPage 11 of 15Next →

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