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A Pyramid Architecture-Based Deep Learning Framework for Breast Cancer Detection.
- Source :
- BioMed Research International; 10/1/2021, p1-10, 10p
- Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- BREAST tumor diagnosis
DEEP learning
PHYSICAL diagnosis
DIGITAL image processing
INFORMATION storage & retrieval systems
MEDICAL databases
SEQUENCE analysis
RESEARCH evaluation
TIME
EARLY detection of cancer
HEALTH outcome assessment
SLIDES (Photography)
CONCEPTUAL structures
DIAGNOSTIC imaging
MEDICAL protocols
PHILOSOPHY of education
DESCRIPTIVE statistics
DECISION making in clinical medicine
DATA analysis software
VIRTUAL microscopy
Subjects
Details
- Language :
- English
- ISSN :
- 23146133
- Database :
- Complementary Index
- Journal :
- BioMed Research International
- Publication Type :
- Academic Journal
- Accession number :
- 152765630
- Full Text :
- https://doi.org/10.1155/2021/2567202