# Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images

arXiv: https://arxiv.org/abs/2001.01599 · HTML: https://mlsanity.com/paper/2001.01599/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2020.

## Built on by (most cited first, 30 of 81)

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- [Towards 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 Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2301.08125/) (2023)
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- [Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need](https://mlsanity.com/paper/2307.02249/) (2023)
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- [Cross-scale Multi-instance Learning for Pathological Image Diagnosis](https://mlsanity.com/paper/2304.00216/) (2023)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
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- [RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.01737/) (2022)
- [Generalizing Nucleus Recognition Model in Multi-source Images via Pruning](https://mlsanity.com/paper/2107.02500/) (2021)
- [DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis](https://mlsanity.com/paper/2109.05788/) (2021)
- [Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2408.09476/) (2024)
- [Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images](https://mlsanity.com/paper/2212.08781/) (2022)
- [Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification](https://mlsanity.com/paper/2306.05029/) (2023)

## Abstract (arXiv)

We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI). The cancer subtype should be classified by referring to a WSI, i.e., a large-sized image (typically 40,000x40,000 pixels) of an entire pathological tissue slide, which consists of cancer and non-cancer portions. One difficulty arises from the high cost associated with annotating tumor regions in WSIs. Furthermore, both global and local image features must be extracted from the WSI by changing the magnifications of the image. In addition, the image features should be stably detected against the differences of staining conditions among the hospitals/specimens. In this paper, we develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks in order to overcome these practical difficulties. When the proposed method was applied to malignant lymphoma subtype classifications of 196 cases collected from multiple hospitals, the classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists.