Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Colorectal cancer is one of the most widespread cancers in western countries. As with most forms of cancers, its diagnosis can only be made by performing a histopathological examination from tissue collected in the suspicious area. This process is time-consuming for the pathologist and consequently for the patient, whereas recent advances in computer vision and machine learning have shown that models can match the performance of human experts in classification and segmentation challenges. However the medical and ethical responsibilities that come with the use of artificial intelligence in healthcare demand that predictions are interpretable, and not just be the output of a black-box model. This paper demonstrates how visualisation of learned features play an important role in making the decision-making process transparent and helps to justify alignment with clinical features of colorectal cancer.

Original publication




Conference paper

Publication Date



690 LNNS


555 - 565