Abstract
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.
Highlights
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples
Researchers have been exploring the implementation of computer-aided diagnosis (CAD) systems for several different tasks regarding cancer whole-slide image (WSI)
Despite the ever-growing number of publications of machine learning (ML) methods applied to CAD systems, there is a dearth of published work for the task of joint detection and classification of colorectal lesions from WSI, lagging colorectal cancer (CRC) behind pathologies such as breast cancer and prostate cancer
Summary
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. The advent of digitised tissue samples, the wider adoption of digital workflows in pathology labs, and the consequent availability of more data, combined with a shortage of pathologists, enabled the evolution of the computational pathology field with the integration of automatic image analysis into clinical practice, mainly based in Artificial Intelligence (AI) methodologies[1,2,3,4]. Despite the ever-growing number of publications of machine learning (ML) methods applied to CAD systems, there is a dearth of published work for the task of joint detection and classification of colorectal lesions from WSI, lagging colorectal cancer (CRC) behind pathologies such as breast cancer and prostate cancer. Since the development of such systems typically requires large and diverse datasets, any review would be incomplete without a concurrent
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