# A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica

arXiv: https://arxiv.org/abs/2107.11696 · HTML: https://mlsanity.com/paper/2107.11696/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 24 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1612.00542/) (2016)
- [What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes](https://mlsanity.com/paper/1503.06410/) (2015)
- [High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks](https://mlsanity.com/paper/1807.03401/) (2018)
- [Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images](https://mlsanity.com/paper/2008.08496/) (2020)
- [Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms](https://mlsanity.com/paper/2006.00086/) (2020)
- [Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis](https://mlsanity.com/paper/1907.00300/) (2019)
- [MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures](https://mlsanity.com/paper/2006.07767/) (2020)
- [More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data](https://mlsanity.com/paper/2104.10223/) (2021)

## Built on by (most cited first, 3 of 3)

- [Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey](https://mlsanity.com/paper/2203.00190/) (2022)
- [Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities](https://mlsanity.com/paper/2109.00889/) (2021)
- [A Novel Dataset for Financial Education Text Simplification in Spanish](https://mlsanity.com/paper/2312.09897/) (2023)

## Abstract (arXiv)

The implementation of deep learning based computer aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a considerable amount of labeled images, which can be expensive to obtain as time and effort from clinical practitioners is required. A number of publicly available datasets have been built with data from different hospitals and clinics. However, using models trained on these datasets for later work on images sampled from a different hospital or clinic might result in lower performance. This is due to the distribution mismatch of the datasets, which include different patient populations and image acquisition protocols. The scarcity of labeled data can also bring a challenge towards the application of transfer learning with models trained using these source datasets. In this work, a real world scenario is evaluated where a novel target dataset sampled from a private Costa Rican clinic is used, with few labels and heavily imbalanced data. The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated. The use of the semi-supervised deep learning approach known as MixMatch, to leverage the usage of unlabeled data from the target dataset, is proposed and evaluated. In the tests, the performance of models is extensively measured, using different metrics to assess the performance of a classifier under heavy data imbalance conditions. It is shown that the use of semi-supervised deep learning combined with fine-tuning can provide a meaningful advantage when using scarce labeled observations. We make available the novel dataset for the benefit of the community.