# Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning

arXiv: https://arxiv.org/abs/1511.05286 · HTML: https://mlsanity.com/paper/1511.05286/

## Key facts

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

## Built on by (most cited first, 30 of 38)

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- [Masked Autoencoders are Scalable Learners of Cellular Morphology](https://mlsanity.com/paper/2309.16064/) (2023)
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- [ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2603.06658/) (2026)
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- [DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learning](https://mlsanity.com/paper/2105.05758/) (2021)
- [HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification](https://mlsanity.com/paper/2103.09764/) (2021)
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- [ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging](https://mlsanity.com/paper/2306.10535/) (2023)
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- [Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling](https://mlsanity.com/paper/2504.09544/) (2025)
- [mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning Techniques](https://mlsanity.com/paper/2105.01443/) (2021)
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- [Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning](https://mlsanity.com/paper/2203.10626/) (2022)
- [Go Beyond Multiple Instance Neural Networks: Deep-learning Models based on Local Pattern Aggregation](https://mlsanity.com/paper/2205.14428/) (2022)
- [mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis](https://mlsanity.com/paper/2405.17824/) (2024)
- [Malignancy Prediction and Lesion Identification from Clinical Dermatological Images](https://mlsanity.com/paper/2104.02652/) (2021)
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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.