# Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations

arXiv: https://arxiv.org/abs/2202.06073 · HTML: https://mlsanity.com/paper/2202.06073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations](https://mlsanity.com/paper/2008.05571/) (2020)
- [Self-Supervised Similarity Learning for Digital Pathology](https://mlsanity.com/paper/1905.08139/) (2019)
- [Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis](https://mlsanity.com/paper/1802.00752/) (2018)
- [Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks](https://mlsanity.com/paper/1710.05726/) (2017)
- [A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images](https://mlsanity.com/paper/1710.01249/) (2017)
- [Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study](https://mlsanity.com/paper/2005.08629/) (2020)
- [Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks](https://mlsanity.com/paper/1804.04595/) (2018)

## Abstract (arXiv)

Breast cancer is one of the leading causes of female mortality in the world. This can be reduced when diagnoses are performed at the early stages of progression. Further, the efficiency of the process can be significantly improved with computer aided diagnosis. Deep learning based approaches have been successfully applied to achieve this. One of the limiting factors for training deep networks in a supervised manner is the dependency on large amounts of expert annotated data. In reality, large amounts of unlabelled data and only small amounts of expert annotated data are available. In such scenarios, transfer learning approaches and self-supervised learning (SSL) based approaches can be leveraged. In this study, we propose a novel self-supervision pretext task to train a convolutional neural network (CNN) and extract domain specific features. This method was compared with deep features extracted using pre-trained CNNs such as DenseNet-121 and ResNet-50 trained on ImageNet. Additionally, two types of patch-combination methods were introduced and compared with majority voting. The methods were validated on the BACH microscopy images dataset. Results indicated that the best performance of 99% sensitivity was achieved for the deep features extracted using ResNet50 with concatenation of patch-level embedding. Preliminary results of SSL to extract domain specific features indicated that with just 15% of unlabelled data a high sensitivity of 94% can be achieved for a four class classification of microscopy images.