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 251–300 of 744 papers

TitleStatusHype
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Certainty Pooling for Multiple Instance Learning—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
Characterizing multiple instance datasets—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
A convex method for classification of groups of examples—0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI—0
Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning—0
Deep Weakly-Supervised Domain Adaptation for Pain Localization in Videos—0
Learning Instance Representation Banks for Aerial Scene Classification—0
A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units—0
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering—0
Learning Person Re-identification Models from Videos with Weak Supervision—0
Deep Multiple Instance Learning with Gaussian Weighting—0
Deep Multiple Instance Learning with Distance-Aware Self-Attention—0
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning—0
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography—0
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets—0
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
Learning from Partial Label Proportions for Whole Slide Image Segmentation—0
A Visual Mining Approach to Improved Multiple-Instance Learning—0
Deep Multiple Instance Learning for Image Classification and Auto-Annotation—0
An Aggregation of Aggregation Methods in Computational Pathology—0
Deep Multiple Instance Learning for Airplane Detection in High Resolution Imagery—0
Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News—0
Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning—0
Learning from Noisy Labels with Noise Modeling Network—0
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction—0
Learning Pretopological Spaces to Model Complex Propagation Phenomena: A Multiple Instance Learning Approach Based on a Logical Modeling—0
Deep Multiple Instance Feature Learning via Variational Autoencoder—0
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks—0
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning—0
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma—0
Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs—0
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology—0
Deep Learning based detection of Acute Aortic Syndrome in contrast CT images—0
Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning—0
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images—0
A Universal Unbiased Method for Classification from Aggregate Observations—0
A Multi-scale Multiple Instance Video Description Network—0
Address Instance-level Label Prediction in Multiple Instance Learning—0
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology—0
Data efficient deep learning for medical image analysis: A survey—0
Audio Event Detection using Weakly Labeled Data—0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—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