Multiple Instance Learning
5개 벤치마크 · 논문 911편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Attention-based Deep Multiple Instance Learning
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Real-world Anomaly Detection in Surveillance Videos
FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
Papers
Conserved Immune Topology Improves Pathology Foundation Model Generalization for Cross-Cancer MSI-H Prediction
Pathology foundation models integrated with multiple instance learning achieve competitive accuracy within single-cancer cohorts, yet cross-cancer generalization remains unresolved due to organ-specific histological and …
Multiple Instance LearningAdaptive Multi-Granularity Temporal Modeling for Weakly Supervised Video Anomaly Detection
As the scale of video surveillance data outpaces manual annotation capacities, weakly supervised video anomaly detection (WSVAD) has emerged as a critical research frontier. Most existing approaches formulate WSVAD withi…
Multiple Instance LearningVideo Anomaly DetectionEvent SegmentationMIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees
Many text classification decisions are viable based on constituent excerpts alone. Taking inspiration from the field of multiple instance learning, we present an algorithm for training a neural network to classify text b…
Multiple Instance LearningText ClassificationIntegrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis
Relational inductive biases are essential for capturing structural dependencies among data. This study investigates a dual-level relational framework for image classification, bridging the gap between implicit representa…
Multiple Instance LearningRepresentation LearningImage ClassificationRepresentation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging
Despite the widespread adoption of foundation models as feature extractors for medical imaging, relatively little is understood about how different pretraining strategies influence the transferability of learned represen…
Diabetic Retinopathy GradingMultiple Instance LearningGroup Equivariant Diffusion for Anomaly Detection in Computational Cytology
Computational cytology on whole-slide images is challenging because malignant cells are rare, heterogeneous, and annotated slides are scarce. Anomaly detection frameworks can be trained on normal slide-negative patches a…
Unsupervised Anomaly DetectionMultiple Instance Learning