A Weakly Supervised Deep Learning Approach for Detecting Malaria and Sickle Cells in Blood Films

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A Weakly Supervised Deep Learning Approach for Detecting Malaria and Sickle Cells in Blood Films

Manescu, P; Bendkowski, C; Claveau, R; Elmi, M; Brown, BJ; Pawar, V; Shaw, MJ; (2020) A Weakly Supervised Deep Learning Approach for Detecting Malaria and Sickle Cells in Blood Films. In: Martel, A and Abolmaesumi, P and Stoyanov, D and Mateus, D and Zuluaga, M and Zhou, SK and Racoceanu, D and Joskowicz, L, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. pp. 226-235). Springer: Lima, Peru. Green open access

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Abstract

Machine vision analysis of blood films imaged under a brightfield microscope could provide scalable malaria diagnosis solutions in resource constrained endemic urban settings. The major bottleneck in successfully analyzing blood films with deep learning vision techniques is a lack of object-level annotations of disease markers such as parasites or abnormal red blood cells. To overcome this challenge, this work proposes a novel deep learning supervised approach that leverages weak labels readily available from routine clinical microscopy to diagnose malaria in thick blood film microscopy. This approach is based on aggregating the convolutional features of multiple objects present in one hundred high resolution image fields. We show that this method not only achieves expert-level malaria diagnostic accuracy without any hard object-level labels but can also identify individual malaria parasites in digitized thick blood films, which is useful in assessing disease severity and response to treatment. We demonstrate another application scenario where our approach is able to detect sickle cells in thin blood films. We discuss the wider applicability of the approach in automated analysis of thick blood films for the diagnosis of other blood disorders.

Type:	Proceedings paper
Title:	A Weakly Supervised Deep Learning Approach for Detecting Malaria and Sickle Cells in Blood Films
Event:	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
ISBN-13:	9783030597214
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1007/978-3-030-59722-1_22
Publisher version:	https://doi.org/10.1007/978-3-030-59722-1_22
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Weak Supervision, Malaria, Sickle Cells, Blood Films, Microscopy
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10114633