ArticleProceedings : Annual International Computer Software and Applications Conference. COMPSAC2020
Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image.
Article in Proceedings : Annual International Computer Software and Applications Conference. COMPSAC, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer.NPJ precision oncology · 2024Article
- Deep learning approaches for breast cancer detection in histopathology images: A review.Cancer biomarkers : section A of Disease markers · 2024Review
- MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors.Sensors (Basel, Switzerland) · 2023Article
- Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.Nature communications · 2022Article
- BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images.Bioengineering (Basel, Switzerland) · 2022Article
- Progress on deep learning in digital pathology of breast cancer: a narrative review.Gland surgery · 2022Review
- A Comparative Study of Multiple Deep Learning Models Based on Multi-Input Resolution for Breast Ultrasound Images.Frontiers in oncology · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
The detection of the region of interests (ROIs) on Whole Slide Images (WSIs) is one of the primary steps in computer-aided cancer diagnosis and grading. Early and accurate identification of invasive cancer regions in WSI is critical in the improvement of breast cancer diagnosis and further improvements in patient survival rates. However, invasive cancer ROI segmentation is a challenging task on WSI because of the low contrast of invasive cancer cells and their high similarity in terms of appearance, to non-invasive regions. In this paper, we propose a CNN based architecture for generating ROIs through segmentation. The network tackles the constraints of data-driven learning and working with very low-resolution WSI data in the detection of invasive breast cancer. Our proposed approach is based on transfer learning and the use of dilated convolutions. We propose a highly modified version of U-Net based auto-encoder, which takes as input an entire WSI with a resolution of 320×320. The network was trained on low-resolution WSI from four different data cohorts and has been tested for inter as well as intra- dataset variance. The proposed architecture shows significant improvements in terms of accuracy for the detection of invasive breast cancer regions.
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Registered trials
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