mlsanityData to 14 Oct 2026

Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning

arXiv 1511.05286 ↗First posted 17 Nov 2015cs.CV q-bio.SC stat.MLcited by 38

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Abstract (arXiv)

Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level. We extend the application of CNNs to microscopy image classification and segmentation using multiple instance learning (MIL). We present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using full resolution microscopy images with global labels. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. We show that training MIL CNNs end-to-end outperforms several previous methods on both mammalian and yeast microscopy images without requiring any segmentation steps.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2015.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 9; 2024: 3; 2025: 2; 2026: 5.

38 papers.

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  1. 2024Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biologycited by 52
  2. 2023RxRx1: A Dataset for Evaluating Experimental Batch Correction Methodscited by 46
  3. 2022Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysiscited by 29
  4. 2022Inference of captions from histopathological patchescited by 26
  5. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Needcited by 22
  6. 2022An Aggregation of Aggregation Methods in Computational Pathologycited by 20
  7. 2022Hierarchical classification at multiple operating pointscited by 14
  8. 2021Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentationcited by 12
  9. 2021Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classificationcited by 12
  10. 2022Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentcited by 11
  11. 2023Masked Autoencoders are Scalable Learners of Cellular Morphologycited by 9
  12. 2022Using Multiple Instance Learning to Build Multimodal Representationscited by 7
26 more
  1. 2021Multi-Attention Multiple Instance Learningcited by 6
  2. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  3. 2026ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imagingcited by 6
  4. 2025Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learningcited by 5
  5. 2021DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learningcited by 4
  6. 2021HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classificationcited by 3
  7. 2023Provable Multi-instance Deep AUC Maximization with Stochastic Poolingcited by 3
  8. 2023ProMIL: Probabilistic Multiple Instance Learning for Medical Imagingcited by 3
  9. 2023Morphological Profiling for Drug Discovery in the Era of Deep Learningcited by 3
  10. 2021COVID-view: Diagnosis of COVID-19 using Chest CTcited by 2
  11. 2025Integrating chemical structures as treatments improves representations of microscopy images for morphological profilingcited by 2
  12. 2021mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning Techniquescited by 1
  13. 2022Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasiacited by 1
  14. 2022Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learningcited by 1
  15. 2022Go Beyond Multiple Instance Neural Networks: Deep-learning Models based on Local Pattern Aggregationcited by 1
  16. 2024mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysiscited by 1
  17. 2021Malignancy Prediction and Lesion Identification from Clinical Dermatological Imagescited by 0
  18. 2021A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide imagescited by 0
  19. 2021Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rayscited by 0
  20. 2023From slides (through tiles) to pixels: an explainability framework for weakly supervised models in pre-clinical pathologycited by 0
  21. 2023Multiple Instance Learning with Trainable Decision Tree Ensemblescited by 0
  22. 2024Bi-capacity Choquet Integral for Sensor Fusion with Label Uncertaintycited by 0
  23. 2026Spatial Blindness in Whole-Slide Multiple Instance Learningcited by 0
  24. 2026GCE-MIL: Faithful and Recoverable Evidence for Multiple Instance Learning in Whole-Slide Imagingcited by 0
  25. 2026Rethinking Global Average Pooling: Your Classifier Is Secretly a Multi-Instance Learnercited by 0
  26. 2026Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathologycited by 0