Multiple Instance Learning

Also known as MIL

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Overview

Multiple Instance Learning (MIL) treats an example as a bag of instances and learns to predict from the bag when the informative regions or moments are unknown. It is useful for tasks with labels for whole slides, scans, videos, or documents but few or no labels for individual patches, slices, frames, or passages. Methods range from mean and max pooling to attention, transformers, graphs, and state-space models, often combined with pretrained features, spatial or temporal structure, prototypes, contrastive learning, and pseudo-labels. A central tension is that strong bag-level predictions do not guarantee faithful instance-level evidence, and more complex aggregators do not always outperform simple pooling.

Latest papers

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96 papers17 papers in the last four weeks, up 89% on the four weeks before. 0.1% of all new papers.

  1. AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image

    Feb 21, 2026Tingting Zheng, Hongxun Yao, Kui Jiang +2Whole-Slide Image AnalysisMultiple Instance Learning

    Harbin Institute of Technology · Tsinghua University · Zhengzhou University

  2. Initialization matters in few-shot adaptation of vision-language models for histopathological image classification

    Feb 21, 2026Pablo Meseguer, Rocío del Amor, Valery NaranjoHistopathology Image ClassificationVLM Adaptation

    Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech), Universitat Politècnica de Valencia, Valencia, Spain · Valencian Graduate School and Research Network for Artificial Intelligence (valgrAI), Valencia, Spain

  3. MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features

    Feb 16, 2026Marcus Jenkins, Jasenka Mazibrada, Bogdan Leahu +1Histopathology Image ClassificationCancer Subtype Classification

    School of Computing Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK · Norfolk and Norwich University Hospital, Norwich, Norfolk, NR4 7UY, UK

  4. MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification

    Feb 16, 2026Mingrui Ma, Chentao Li, Pan Huang +1Histopathology Image ClassificationExplainable Medical Image Analysis

    School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China · Affiliated Tumor Hospital of Xinjiang Medical University, Xinjiang 830011, China · Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA +1

  5. Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognition

    Feb 16, 2026Chentao Li, Pan HuangWhole-Slide Image AnalysisHistopathology Image Classification

  6. Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning

    Dec 19, 2025Wei Tang, Yin-Fang Yang, Weijia Zhang +1Model CalibrationWeakly Supervised Learning

    School of Computer Science and Engineering, Southeast University, Nanjing 210096, China, and the Key Laboratory of Computer Network and Information Integration (Southeast University), MoE, China · School of Computer and Information Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia