Mr. Fabian León Profile

Fabian León

at Univ Industrial de Santander

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Towards reduction of expert bias on Gleason score classification via a semi-supervised deep learning strategy

Miguel Plazas, Raúl Ramos-Pollán, Fabian León, Fabio Martínez

Proceedings Volume 12032, 120322O (2022) https://doi.org/10.1117/12.2611517

KEYWORDS: Visualization, Statistical modeling, Cancer, Visual process modeling, Solid state lighting, Data modeling, Tissues, Solids, Prostate cancer, Imaging systems

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Gleason Score (GS) is the principal histological grading system to support the quantification of cancer aggressiveness. This analysis is carried out by expert pathologists but with reported evidence of moderate agreement among pathologists (kappa values less than 0.5). This fact can be prone to errors that directly affect the diagnosis and subsequent treatment. Current deep learning approaches have been proposed to support such visual pattern quantification but there exist a remarked on expert annotations that overfit representations. Besides, the supervised representation is limited to model the high reported visual variability intra Gleason grades. This work introduces a semi-Supervised Learning (SSL) approach that initially uses a reduced set of annotated visual patterns to built several GS deep representations. Then, the set of deep models automatically propagates annotations to unlabeled patches. The most confident predicted samples are used to retrain the ensemble deep representation. Over a patch-based framework with a total of 26259 samples, coded from 886 tissue microarrays, the proposed approach achieved remarkable results between grades three and four. Interestingly, the proposed SSL with only the 10% of samples achieves more general representation, achieving averages scores of ~75.93% and ∼ 71.88% concerning two expert pathologists

Proceedings Article | 3 January 2020 Paper

An inception deep architecture to differentiate close-related Gleason prostate cancer scores

Fabian León, Miguel Plazas, Fabio Martínez

Proceedings Volume 11330, 113300D (2020) https://doi.org/10.1117/12.2547113

KEYWORDS: Prostate cancer, Cancer, Tissues, Tumors

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Histopathological tissue analysis is the most effective and definitive method to prognosis cancer and stratify the aggressiveness of the disease. The Gleason Score (GS) is the most powerful grading system based on architectural tumor pattern quantification. This score characterizes cancer tumor tissue, such as the level of cell differentiation on histopathological images. The reported GS is described as the sum of two principal grades present in a particular image, and ranged from 6 (cancer grow slowly) to 10 (cancer cells spread more rapidly). A main drawback of GS is the pathological dependency on histopathological region stratification, which strongly impacts the clinical procedure to treat the disease. The agreement among experts has been quantified with a kappa index of: ~0.71. Even worse, a higher uncertainty is reported for intermediate grade stratification. This work presents a like-inception deep architecture that is able to differentiate between intermediate and close GS grades. Each image herein evaluated was split-up into regional patches that correspond to a single GS grade. A set of training patches were augmented according to appearance image variations of each grade. Then, a transfer learning scheme was implemented to adapt a bi-Gleason tumor patterns prediction among close levels. The proposed approach was evaluated on public set of 886 tissue H&E stained images with different GS grades, achieving an average accuracy of 0.73% between grades three and four.

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