| dc.description.abstract |
Burkitt Lymphoma (BL) is a highly treatable cancer. However, delayed diagnosis
of BL contributes to high mortality in BL endemic regions of Africa. Lack of
enough pathologists in the region is a major reason for delayed diagnosis. The
work described in this paper is a proof-of-concept study to develop a targeted,
open access AI tool for screening of histopathology slides in suspected BL cases.
Slides were obtained from a total of 90 BL patients. 70 Tonsillectomy samples
were used as controls. We fine-tuned 6 pre-trained models and evaluated the
performance of all 6 models across different configurations. An ensemble-
based consensus approach ensured a balanced and robust classification. The
tool applies novel features to BL diagnosis including use of multiple image
magnifications, thus enabling use of different magnifications of images based
on the microscope/scanner available in remote clinics, composite scoring of
multiple models and utilizing MIL with weak labeling and image augmentation,
enabling use of relatively low sample size to achieve good performance on the
inference set. The open access model allows free access to the AI tool from
anywhere with an internet connection. The ultimate aim of this work is making
pathology services accessible, efficient and timely in remote clinics in regions
where BL is endemic. New generation of low-cost slide scanners/microscopes
is expected to make slide images available immediately for the AI tool for
screening and thus accelerate diagnosis by pathologists available locally or
online. |
en_US |