2023
DOI: 10.1016/j.semcancer.2023.06.004
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Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives

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Cited by 8 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%
“…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%