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BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights
تشخیص خودکار سرطان پستان مبتنی بر BreakHis با استفاده از یادگیری عمیق: طبقه بندی ، مرور و بینش-2019 There are several breast cancer datasets for building Computer Aided Diagnosis systems (CADs) using either deep learning or traditional models. However, most of these datasets impose various trade-offs on practitioners related to their availability or inner clinical value. Recently, a public dataset called BreakHis has been released to overcome these limitations. BreakHis is organized into four magnifica- tion levels, each image is labeled according to its main category (Benign/Malignant) and its subcategory (A/F/PT/TA/PC/DC/LC/MC). This organization allows practitioners to address this problem either as a bi- nary or a multi-category classification task with either a magnification dependent or independent train- ing approach. In this work, we define a taxonomy that categorize this problem into four different refor- mulations: Magnification-Specific Binary (MSB), Magnification-Independent Binary (MIB), Magnification- Specific Multi-category (MSM) and Magnification-Independent Multi-category (MIM) classifications. We provide a comprehensive survey of all related works. We identify the best reformulation from clinical and practical standpoints. Finally, we explore for the first time the MIM approach using deep learning and draw the learnt lessons. Keywords: Breast cancer | BreakHis dataset | Histopathological images | Computer aided diagnosis | Deep learning | Data preprocessing |
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