# DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization

arXiv: https://arxiv.org/abs/2110.00109 · HTML: https://mlsanity.com/paper/2110.00109/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction](https://mlsanity.com/paper/1907.02757/) (2019)
- [Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels](https://mlsanity.com/paper/1512.06974/) (2015)
- [Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning](https://mlsanity.com/paper/1804.03830/) (2018)
- [Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition](https://mlsanity.com/paper/1701.06599/) (2017)
- [Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification](https://mlsanity.com/paper/1906.03359/) (2019)
- [Y-net: Biomedical Image Segmentation and Clustering](https://mlsanity.com/paper/2004.05698/) (2020)
- [Unsupervised Task Design to Meta-Train Medical Image Classifiers](https://mlsanity.com/paper/1907.07816/) (2019)
- [Unsupervised deep clustering for predictive texture pattern discovery in medical images](https://mlsanity.com/paper/2002.03721/) (2020)

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Complex-valued Federated Learning with Differential Privacy and MRI Applications](https://mlsanity.com/paper/2110.03478/) (2021)
- [Text-Guided Image Clustering](https://mlsanity.com/paper/2402.02996/) (2024)
- [FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning](https://mlsanity.com/paper/2310.12600/) (2023)
- [Self Supervised Correlation-based Permutations for Multi-View Clustering](https://mlsanity.com/paper/2402.16383/) (2024)
- [Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures](https://mlsanity.com/paper/2403.14830/) (2024)
- [Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data?](https://mlsanity.com/paper/2412.15967/) (2024)

## Abstract (arXiv)

In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods. This is mainly due to the fact that ground-truth labels are time-consuming and expensive to obtain manually. Generating labels from patient metadata might be feasible but it suffers from user-originated errors which introduce biases. In this work, we propose an unsupervised approach for automatically clustering and categorizing large-scale medical image datasets, with a focus on cardiac MR images, and without using any labels. We investigated the end-to-end training using both class-balanced and imbalanced large-scale datasets. Our method was able to create clusters with high purity and achieved over 0.99 cluster purity on these datasets. The results demonstrate the potential of the proposed method for categorizing unstructured large medical databases, such as organizing clinical PACS systems in hospitals.