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 301–350 of 744 papers

TitleStatusHype
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—0
CytoFM: The first cytology foundation model—0
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms—0
Cross-Modal Retrieval with Implicit Concept Association—0
Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology—0
Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification—0
Attention-effective multiple instance learning on weakly stem cell colony segmentation—0
A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images—0
Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection—0
Multi-Attention Multiple Instance Learning—0
Instance Significance Guided Multiple Instance Boosting for Robust Visual Tracking—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances—0
MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning—0
InfoMask: Masked Variational Latent Representation to Localize Chest Disease—0
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations—0
Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images—0
Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks—0
Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision—0
Multiple instance learning for sequence data with across bag dependencies—0
MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation—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
Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis—0
CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation—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
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images—0
Human versus Machine Attention in Document Classification: A Dataset with Crowdsourced Annotations—0
Convex Multiple-Instance Learning by Estimating Likelihood Ratio—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
Attention-based Multiple Instance Learning with Mixed Supervision on the Camelyon16 Dataset—0
Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning—0
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification—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
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology—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
MECFormer: Multi-task Whole Slide Image Classification with Expert Consultation Network—0
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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