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Multiple Instance Learning

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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

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Multiple Instance Learning

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Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning

The Trilemma of Truth in Large Language Models

OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport

Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping

MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis

HyperPath: Knowledge-Guided Hyperbolic Semantic Hierarchy Modeling for WSI Analysis

Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping

BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models

Do MIL Models Transfer?

Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis

Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology

PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset

DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset

FMG-Det: Foundation Model Guided Robust Object Detection

Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans

Self-Classification Enhancement and Correction for Weakly Supervised Object Detection

Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification

Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging

VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction

Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysis

ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications

Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study

A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology

How Effective Can Dropout Be in Multiple Instance Learning ?

CytoFM: The first cytology foundation model

Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision

Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network

P2Object: Single Point Supervised Object Detection and Instance Segmentation

A Survey of Pathology Foundation Model: Progress and Future Directions

Leveraging Gait Patterns as Biomarkers: An attention-guided Deep Multiple Instance Learning Network for Scoliosis Classification

MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning

Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology

Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology

PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification

SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis