Biology

Taxonomy (biology)

Support vector machine

Binary classification

Magnification

Computer science

Botany

Pattern recognition (psychology)

Mathematics

Categorization

Internal medicine

Artificial intelligence

Medicine

Deep learning

Breast cancer

Binary number

Cancer

Machine learning

Arithmetic

Neurocomputing

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 magnification 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 binary or a multi-category classification task with either a magnification dependent or independent training approach. In this work, we define a taxonomy that categorize this problem into four different reformulations: 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.

BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights