# A survey on deep learning approaches for breast cancer diagnosis

arXiv: https://arxiv.org/abs/2109.08853 · HTML: https://mlsanity.com/paper/2109.08853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 102 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Rethinking the Value of Network Pruning](https://mlsanity.com/paper/1810.05270/) (2018)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Improving the Robustness of Deep Neural Networks via Stability Training](https://mlsanity.com/paper/1604.04326/) (2016)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes](https://mlsanity.com/paper/1711.08580/) (2017)
- [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018)
- [Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model](https://mlsanity.com/paper/2011.07995/) (2020)
- [Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network](https://mlsanity.com/paper/1809.01687/) (2018)
- [Joint 2D-3D Breast Cancer Classification](https://mlsanity.com/paper/2002.12392/) (2020)
- [2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification](https://mlsanity.com/paper/2002.12314/) (2020)
- [Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural Network](https://mlsanity.com/paper/2101.03541/) (2021)

## Built on by (most cited first, 1 of 1)

- [BCDNet: A Fast Residual Neural Network For Invasive Ductal Carcinoma Detection](https://mlsanity.com/paper/2408.13800/) (2024)

## Abstract (arXiv)

Deep learning has introduced several learning-based methods to recognize breast tumours and presents high applicability in breast cancer diagnostics. It has presented itself as a practical installment in Computer-Aided Diagnostic (CAD) systems to further assist radiologists in diagnostics for different modalities. A deep learning network trained on images provided by hospitals or public databases can perform classification, detection, and segmentation of lesion types. Significant progress has been made in recognizing tumours on 2D images but recognizing 3D images remains a frontier so far. The interconnection of deep learning networks between different fields of study help propels discoveries for more efficient, accurate, and robust networks. In this review paper, the following topics will be explored: (i) theory and application of deep learning, (ii) progress of 2D, 2.5D, and 3D CNN approaches in breast tumour recognition from a performance metric perspective, and (iii) challenges faced in CNN approaches.