Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology

arXiv:2609.31712 · cs.CV, cs.LG, stat.ML · Submitted 2026-09-21 · Read on arXiv

cs.CV, cs.LG, stat.ML

Submitted: 2026-09-21

Updated: 2026-09-21

Terminology

• Ablation — 42× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Post-Selection — 8× in this paper · explained in 2 episodes
• Adaptive Selection — 7× in this paper · explained in 1 episode
• Attention Mechanism — 6× in this paper · explained in 2 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Feature Selection — 5× in this paper · explained in 4 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Selection Bias — 3× in this paper · explained in 4 episodes
• Core — 2× in this paper · explained in 2 episodes
• Multiple Instance Learning (MIL) — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint
• Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
• si4onnx: A Python package for Selective Inference in Deep Learning Models
• Optimal Inference After Model Selection

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models