A multi-context CNN ensemble for small lesion detection

Artificial Intelligence in Medicine
B SavelliF Tortorella

Abstract

In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities.

Citations

Mar 7, 2020·Journal of Medical Radiation Sciences·Dennis Jay WongWarren Reed
Feb 26, 2021·Computers in Biology and Medicine·Leila AbdelrahmanMohamed Abdel-Mottaleb
Mar 10, 2021·Artificial Intelligence in Medicine·Pardha Saradhi Mittapalli, Thanikaiselvan V
Jun 15, 2021·Computers in Biology and Medicine·Behrouz RostamiZeyun Yu
Aug 21, 2021·Artificial Intelligence in Medicine·Yang HuWendy Hall

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