CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Sara P. Oliveira,Pedro C. Neto,João Fraga,Diana Montezuma,Ana Monteiro,Joao M. Monteiro,Liliana Ribeiro,Sofia Gonçalves,Isabel M. Pinto,Jaime S. Cardoso +9 more
TLDR
In this article, the authors analyzed some relevant works published on this particular task and highlighted the limitations that hinder the application of these works in clinical practice, and empirically investigated the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for colorectal cancer from WSI.Abstract:
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.read more
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Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations
Niccolò Marini,Stefano Marchesin,Sebastian Otálora,Marek Wodzinski,Alessandro Caputo,Mart van Rijthoven,Witali Aswolinskiy,John-Melle Bokhorst,Damian Podareanu,Edyta Petters,Svetla Boytcheva,G. Buttafuoco,Simona Vatrano,Filippo Fraggetta,J. A. W. M. van der Laak,Maristella Agosti,Francesco Ciompi,Gianmaria Silvello,H. Muller,Manfredo Atzori +19 more
TL;DR: In this paper , the authors proposed and evaluated an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology, which includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis.
Journal ArticleDOI
Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review
Athena Davri,Effrosyni Birbas,Theofilos Kanavos,Georgios Ntritsos,Nikolaos Giannakeas,Alexandros T. Tzallas,Anna Batistatou +6 more
TL;DR: This work aims to systematically review the current research on AI in CRC image analysis to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
Journal ArticleDOI
Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities
Diana Montezuma,Ana Rita Coelho e Silva Honório Monteiro,João Fraga,Liliana Ribeiro,Sofia Gonçalves,André Tavares,João Monteiro,Isabel Macedo-Pinto +7 more
TL;DR: In this article , the authors report their experience in digital pathology transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues.
Proceedings ArticleDOI
Deep Ordinal Focus Assessment for Whole Slide Images
Journal ArticleDOI
Data-driven color augmentation for H&E stained images in computational pathology
Niccolò Marini,Sebastian Otálora,Marek Wodzinski,Selene Tomassini,Aldo Franco Dragoni,Stéphane Marchand-Maillet,Juan Pedro Dominguez Morales,Lourdes Duran-Lopez,Simona Vatrano,Henning Müller,Manfredo Atzori +10 more
TL;DR: In this paper , a Data-Driven Color Augmentation (DDCA) method was proposed to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models.
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