This article examines the prospect of clinical acceptability of the automated diagnosis of histopathological images by reviewing and critically analyzing the extant literature devoted to context-aware deep learning approaches. Analogous to unboxing the black-boxes, the context-aware methods in the deep learning models introduce transparency in the process, which builds trust and thus encourages acceptance of the method among the clinicians. This review highlights the context-aware approaches researchers adopt to disentangle the convoluted strands of complex algorithms and their efficacy as a whole. To detect cancers, classical machine learning algorithms employ handcrafted features, while deep learning algorithms generate complex representations of objects from simple features. Analysis of a histological image for cancer detection, using morphological and textural features of cells, can be enhanced by including contextual information. In medical image analysis, the focus has primarily been on handcrafted or learned features, largely devoid of explicit context for object representation. To delve into this scantily addressed domain of “context", this study rests on knowledge-driven and adaptive smart features for classifying histological images. This work explores available algorithms that incorporate contextual features for diagnosing breast cancer. Accordingly, a high-level block diagram is devised to aid in selecting an algorithm vis-à-vis an application that would fulfill a vital need for researchers and industry professionals alike. Furthermore, this study proposes the upcoming trend of automated diagnosis in the domain of cancer.
Dutta et al. (Fri,) studied this question.