# Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis

arXiv: https://arxiv.org/abs/2406.18212 · HTML: https://mlsanity.com/paper/2406.18212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [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)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Making Convolutional Networks Shift-Invariant Again](https://mlsanity.com/paper/1904.11486/) (2019)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [TResNet: High Performance GPU-Dedicated Architecture](https://mlsanity.com/paper/2003.13630/) (2020)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://mlsanity.com/paper/1712.02616/) (2017)
- [Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides](https://mlsanity.com/paper/2112.02222/) (2021)
- [Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis](https://mlsanity.com/paper/1802.00752/) (2018)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [Nuclei Detection Using Mixture Density Networks](https://mlsanity.com/paper/1808.08279/) (2018)
- [Classification of Breast Cancer Histology using Deep Learning](https://mlsanity.com/paper/1802.08080/) (2018)
- [Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network](https://mlsanity.com/paper/2208.00594/) (2022)
- [Face Recognition Using Map Discriminant on YCbCr Color Space](https://mlsanity.com/paper/1807.02135/) (2018)
- [Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning](https://mlsanity.com/paper/2109.06547/) (2021)

## Abstract (arXiv)

Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal growth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph node (ALN) status, and Molecular subtype (MS) can play a significant role in improved BC diagnosis. However, the existing methods predict only a single factor which makes them less suitable to use in diagnosis and designing a strategy for treatment. In this paper, we propose to classify the six essential indicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using H&E stained WSI's. To precisely capture local neighboring relationships, we use spatial and frequency domain information from the large patch size of WSI's malignant regions. Furthermore, to cater the variable number of regions of interest sizes and give due attention to each region, we propose a malignant region learning attention network. Our experimental results demonstrate that combining spatial and frequency information using the malignant region learning module significantly improves multi-factor and single-factor classification performance on publicly available datasets.