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Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis

arXiv 2406.18212 ↗First posted 26 Jun 2024eess.IV cs.CVcited by 0

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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.

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.

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21 of 53 references.

References · 21

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9 more
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  2. 2021Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slidescited by 43
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