2022
DOI: 10.3390/cancers14102489
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iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images

Abstract: Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpreta… Show more

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Cited by 16 publications
(21 citation statements)
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“…and had a high consensus with experts. Subsequent studies have also applied MIL for Gleason grading [ 80 , 81 ], grading dysplasia of various cancers (i.e., normal, low-grade, and high-grade) [ 82 , 83 ], and grading of colorectal cancer (i.e., low-grade and high-grade) [ 84 ].…”
Section: Related Workmentioning
confidence: 99%
“…and had a high consensus with experts. Subsequent studies have also applied MIL for Gleason grading [ 80 , 81 ], grading dysplasia of various cancers (i.e., normal, low-grade, and high-grade) [ 82 , 83 ], and grading of colorectal cancer (i.e., low-grade and high-grade) [ 84 ].…”
Section: Related Workmentioning
confidence: 99%
“…On the other hand, it is easier to label the dataset at the slide level. The inclusion of detailed spatial annotations on approximately 10% of the dataset has been shown to positively impact the performance of deep learning algorithms [17,31]. To fully leverage the potential of spatial and slide labels, we propose a deep learning pipeline, based on previous approaches [17,31], using mixed supervision.…”
Section: Problem Definitionmentioning
confidence: 99%
“…The inclusion of detailed spatial annotations on approximately 10% of the dataset has been shown to positively impact the performance of deep learning algorithms [17,31]. To fully leverage the potential of spatial and slide labels, we propose a deep learning pipeline, based on previous approaches [17,31], using mixed supervision. Each slide, S is composed of a set of tiles T s,n , where s represents the index of the slide and n ∈ {1, • • • , n s } the tile number.…”
Section: Problem Definitionmentioning
confidence: 99%
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