Artículos (Arquitectura y Tecnología de Computadores)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11292

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Examinando Artículos (Arquitectura y Tecnología de Computadores) por Autor "Atzori, Manfredo"

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    Acceso AbiertoPremio Mensual Publicación Científica Destacada de la US. Escuela Técnica Superior de Ingeniería InformáticaArtículo
    A systematic comparison of deep learning methods for Gleason grading and scoring
    (Elsevier, 2024-07) Domínguez Morales, Juan Pedro; Durán López, Lourdes; Marini, Niccolo; Vicente Díaz, Saturnino; Linares Barranco, Alejandro; Atzori, Manfredo; Muller, Henning; ; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores; Junta de Andalucía; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; European Union (UE). H2020; Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores
    Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.
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    Acceso AbiertoArtículo
    Data-driven color augmentation for H&E stained images in computational pathology
    (ScienceDirect, 2023) Marini, Niccolò; Otalora, Sebastián; Wodzinski, Marek; Tomassini, Selene; Franco Dragoni, Aldo; Marchand Maillet, Stephane; Domínguez Morales, Juan Pedro; Durán López, Lourdes; Vatrano, Simona; Müller, Henning; Atzori, Manfredo; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores; European Commission (EC)
    Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers. Dealing with stain color heterogeneity often limits the robustness of methods developed to analyze WSIs, in particular Convolutional Neural Networks (CNN), the state-of-the-art algorithm for most computational pathology tasks. Stain color heterogeneity is still an unsolved problem, although several methods have been developed to alleviate it, such as Hue-Saturation-Contrast (HSC) color augmentation and stain augmentation methods. The goal of this paper is to present Data-Driven Color Augmentation (DDCA), a method to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. DDCA is applied to HSC color augmentation, stain augmentation and H&E-adversarial networks in colon and prostate cancer classification tasks. DDCA is then compared with 11 state-of-the-art baseline methods to handle color heterogeneity, showing that it can substantially improve classification performance on unseen data including heterogeneous color variations.