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
An In-field Automatic Wheat Disease Diagnosis System—0
An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images—0
In Defense of LSTMs for Addressing Multiple Instance Learning Problems—0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation—0
Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures—0
Anomaly Detection with Inexact Labels—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
A Proposal-Based Paradigm for Self-Supervised Sound Source Localization in Videos—0
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations—0
A Sample-Based Training Method for Distantly Supervised Relation Extraction with Pre-Trained Transformers—0
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection—0
A self-supervised framework for learning whole slide representations—0
A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes—0
Attention Awareness Multiple Instance Neural Network—0
Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysis—0
Attention-based Multiple Instance Learning with Mixed Supervision on the Camelyon16 Dataset—0
Attention-effective multiple instance learning on weakly stem cell colony segmentation—0
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms—0
Audio Event Detection using Weakly Labeled Data—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
A Universal Unbiased Method for Classification from Aggregate Observations—0
Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning—0
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images—0
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning—0
Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning—0
A Visual Mining Approach to Improved Multiple-Instance Learning—0
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning—0
A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification—0
Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Images—0
Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data—0
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models—0
Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification—0
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling—0
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models—0
Boosting Point-Supervised Temporal Action Localization through Integrating Query Reformation and Optimal Transport—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification—0
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images—0
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer—0
Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and L_1 Regularization—0
CanvOI, an Oncology Intelligence Foundation Model: Scaling FLOPS Differently—0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
Cascade Attentive Dropout for Weakly Supervised Object Detection—0
Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning—0
Certainty Pooling for Multiple Instance Learning—0
Characterizing multiple instance datasets—0
Characterizing the Interpretability of Attention Maps in Digital Pathology—0
CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images—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