# Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography

arXiv: https://arxiv.org/abs/2008.02957 · HTML: https://mlsanity.com/paper/2008.02957/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Attention-Map Augmentation for Hypercomplex Breast Cancer Classification](https://mlsanity.com/paper/2310.07633/) (2023)
- [Progressive Dual Priori Network for Generalized Breast Tumor Segmentation](https://mlsanity.com/paper/2310.13574/) (2023)
- [Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction](https://mlsanity.com/paper/2603.11206/) (2026)
- [Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review](https://mlsanity.com/paper/2210.11943/) (2022)
- [OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation](https://mlsanity.com/paper/2310.02486/) (2023)

## Abstract (arXiv)

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis (CAD) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional consideration of important shape and boundary knowledge. One path called the Locality Preserving Learner (LPL), is devoted to hierarchically extracting and exploiting intrinsic features of the input. Whereas the other path, called the Conditional Graph Learner (CGL) focuses on generating geometrical features via modeling pixel-wise image to mask correlations. By integrating the two learners, both the semantics and structure are well preserved and the component learning paths in return complement each other, contributing an improvement to the mass segmentation and cancer classification problem at the same time. We evaluated our method on two most used public mammography datasets, DDSM and INbreast. Experimental results show that DualCoreNet achieves the best mammography segmentation and classification simultaneously, outperforming recent state-of-the-art models.