Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives

Doeleman, Thom; Hondelink, Liesbeth M.; Vermeer, Maarten H.; Dijk, Marijke R. van; Schrader, Anne M.R.

doi:10.1016/j.semcancer.2023.06.004

Cited by 6 publications

(1 citation statement)

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“…Leveraging the capabilities of contemporary deep learning techniques ( 15–17 ), we provide proof of concept for an AI model that would efficiently analyze histopathological images extracted from biopsy samples. The objective of the deep learning model we present here is to classify patient tissue sections as either BL or non-BL with high accuracy and sensitivity/recall ( 18–20 ). Our method is innovative in that it uses a significantly lower sample size (less than 160 patient samples) to train the model than is usually reported, but still captures the variance in the data and attains good performance on inference testing.…”

Section: Introductionmentioning

confidence: 99%

An AI based, open access screening tool for early diagnosis of Burkitt lymphoma

Nambiar,

Rajesh,

Nair

et al. 2024

Front. Med.

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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.

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Section: Introductionmentioning

confidence: 99%