Papers Multiple Instance Learning
“Multiple Instance Learning” 태그가 달린 논문 912편 · 필터 해제
Unifying Semantic Priors and High-Frequency Traces: Enhancing V-JEPA with Mixture-of-Experts for Robust Synthetic Image Forensics
The unchecked proliferation of manipulated images on social media platforms has increased the spread of misinformation, posing a severe threat to public trust and information integrity. Modern deepfake detectors typicall…
Multiple Instance LearningDeepFake DetectionConserved 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 LearningWeakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary Labels
In prostate cancer histopathology, the Gleason Score is determined by the most frequent (Primary) and second most frequent (Secondary) Gleason patterns within a whole-slide image. Although these slide-level labels are ro…
Multiple Instance LearningExplainable graph attention network for stress recognition (StressGAT) via differential action units
Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), often ov…
Multiple Instance LearningPretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology. However, existing MIL aggregators are still typically trained from scratch for each downstrea…
Multiple Instance LearningComputational EfficiencyDINOv3-MIL: Per-Kidney Multi-Label Tumour and Cyst Detection from Foundation-Model Patch Tokens on KiTS23
Foundation vision models trained on natural images transfer to medical tasks without domain pre-training, but volumetric classification requires aggregating tens of thousands of patch tokens per study, and the aggregator…
Multiple Instance LearningCGRL: Concept-Guided Pruning and Representation Learning for Whole-Slide Image Classification
Weakly supervised whole-slide image (WSI) classification is widely used in computational pathology because slide-level labels are easier to obtain than dense region annotations. Existing multiple instance learning (MIL) …
Multiple Instance LearningRepresentation LearningImage ClassificationLearning from Lost Provenance: Multiple Instance Learning for Cancer Registry Tumor Group Classification
Modernizing cancer registries with deep learning is opening new opportunities to automate labor-intensive tasks such as the coding of pathology reports. However, progress is constrained by the scarcity of report-level hu…
Multiple Instance LearningLatent Clarity: Bridging World-Model Kinematics to Semantic Manifolds for Video Anomaly Anticipation
Continuous video anomaly detection is dominated by reactive Multiple Instance Learning (MIL) that collapses spatiotemporal features into scalar scores. We introduce PULS (Predictive Unified Latent Space), a continuous se…
Multiple Instance LearningVideo Anomaly DetectionTaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
Whole slide image (WSI) analysis is central to computational pathology, with multiple instance learning (MIL) emerging as the standard pipeline for slide-level diagnosis. However, conventional approaches formulate WSI di…
Multiple Instance LearningText GenerationRethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition
In this paper, we present the solution developed by our team, XInsight Lab, which achieved first place in Track 3 of the 4th EI-MIGA-IJCAI Challenge with a test accuracy of 0.76923. To address the challenge of weak and s…
Multiple Instance LearningEmotion RecognitionFeature EngineeringLearning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection
Weakly supervised video anomaly detection (WSVAD) has predominantly focused on temporal localization, identifying when anomalies occur while largely neglecting their spatial extent within frames. Yet, spatial localizatio…
Multiple Instance LearningVideo Anomaly DetectionPredicting Metastatic Risk from Primary Tissue Architecture via Distance-Aware Spatial Modeling
Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology. Multiple Instance Learning (MIL) approaches can attend to subdomains in tumor r…
Multiple Instance LearningThe Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment
Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive stepwise annotations. Outcome-supervised …
Multiple Instance LearningEvery Step of the Way: Video-based Parkinsonian Turning Step Counting
As a prominent symptom of Parkinson's disease (PD), turning impairment is evaluated through parameters such as turning angle, duration, and particularly, the number of steps required to complete a turn, which directly re…
Multiple Instance LearningHuman Mesh Recovery