A pyramid architecture-based deep learning framework for breast cancer detection
Open accessAbstract
Breast cancer diagnosis is a critical step in clinical decision making, and this is achieved by making a pathological slide and gives a decision by the doctors, which is the method of final decision making for cancer diagnosis. Traditionally, the doctors usually check the pathological images by visual inspection under the microscope. Whole-slide images (WSIs) have supported the state-of-the-art diagnosis results and have been admitted as the gold standard clinically. However, this task is time-consuming and labour-intensive, and all of these limitations make low efficiency in decision making. Medical image processing protocols have been used for this task during the last decades and have obtained satisfactory results under some conditions; especially in the deep learning era, it has exhibited the advantages than those in the shallow learning period. In this paper, we proposed a novel breast cancer region mining framework based on deep pyramid architecture from multilevel and multiscale breast pathological WSIs. We incorporate the tissue- and cell-level information together and integrate these into a LSTM model for the final sequence modelling, which successfully keeps the WSIs' integration and is not mentioned by the prevalence frameworks. The experiment results demonstrated that our proposed framework greatly improved the detection accuracy than that only using tissue-level information.
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Groups have reported deep learning for breast cancer diagnosis and subtyping from H&E slides and for quantifying features such as mitotic cell counting.
“groups have reported on the use of deep learning for breast cancer diagnosis and subtyping from H&E slides (49)(50)(51), and quantifying other clinically relevant features such as mitotic cell counting (52,53).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [49]
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- Authors
- Sui, D., Liu, W., Chen, J., Zhao, C., Ma, X., Guo, M., & Tian, Z.
- Affiliations
- Beijing University of Civil Engineering and Architecture (CN)
- Changhai Hospital (CN)
- Corresponding author
- Sui, Dong
- Tian, Zhaofeng
- Venue
- BioMed Research International
- Publisher
- Wiley
- Published
- 2021-01
- Volume
- 2021
- Issue
- 1
- Pages
- 2567202-2567202
- ISSN
- 1110-7243 (ISSN-L)
- 2314-6133
- 2314-6141
- PMC
- PMC8500767
- OpenAlex
- W3201755143
- Cited by
- 29
- References
- 32
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 6 · 2021: 1
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- BioMed Research International (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Beijing University of Civil Engineering and Architecture (X20040, JDYC20200318)
- National Natural Science Foundation of China (62031003, 61702026)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer detection, whole slide images, pyramid architecture, multiscale image analysis, breast histopathology, LSTM, computer-aided diagnosis
- MeSH terms
- Deep Learning, Algorithms, Breast Neoplasms, Female, Humans, Image Processing, Computer-Assisted
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