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 601–650 of 744 papers

TitleStatusHype
Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision—0
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations—0
InfoMask: Masked Variational Latent Representation to Localize Chest Disease—0
Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances—0
Instance Significance Guided Multiple Instance Boosting for Robust Visual Tracking—0
Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks—0
Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification—0
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images—0
Is Attention Interpretation? A Quantitative Assessment On Sets—0
Isoform Function Prediction Using a Deep Neural Network—0
JCDNet: Joint of Common and Definite phases Network for Weakly Supervised Temporal Action Localization—0
Joint Multiple Intent Detection and Slot Filling via Self-distillation—0
Kernel Self-Attention in Deep Multiple Instance Learning—0
Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology—0
Label Stability in Multiple Instance Learning—0
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning—0
Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning—0
Learning from Noisy Labels with Noise Modeling Network—0
Learning from Partial Label Proportions for Whole Slide Image Segmentation—0
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction—0
Learning Instance Representation Banks for Aerial Scene Classification—0
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering—0
Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning—0
Learning Person Re-identification Models from Videos with Weak Supervision—0
Learning Pretopological Spaces to Model Complex Propagation Phenomena: A Multiple Instance Learning Approach Based on a Logical Modeling—0
Learning Time Series Detection Models from Temporally Imprecise Labels—0
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision—0
Learning to Detect Semantic Boundaries with Image-level Class Labels—0
Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification—0
Learning to quantify emphysema extent: What labels do we need?—0
Learning to Select Cuts for Efficient Mixed-Integer Programming—0
Leveraging Gait Patterns as Biomarkers: An attention-guided Deep Multiple Instance Learning Network for Scoliosis Classification—0
Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions—0
LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information—0
LNPL-MIL: Learning from Noisy Pseudo Labels for Promoting Multiple Instance Learning in Whole Slide Image—0
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection—0
LOMo: Latent Ordinal Model for Facial Analysis in Videos—0
Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis—0
Towards Train-Test Consistency for Semi-supervised Temporal Action Localization—0
Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics—0
Machine learning identification of maternal inflammatory response and histologic choroamnionitis from placental membrane whole slide images—0
MECFormer: Multi-task Whole Slide Image Classification with Expert Consultation Network—0
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification—0
Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling—0
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images—0
MicroMIL: Graph-based Contextual Multiple Instance Learning for Patient Diagnosis Using Microscopy Images—0
MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation—0
MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning—0
Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis—0
Mixed Supervised Object Detection with Robust Objectness Transfer—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