Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

Autor: Mosquera-Zamudio A; Universitat de València, Valencia, Spain.; INCLIVA, Instituto de Investigación Sanitaria, Valencia, Spain., Launet L; Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain., Colomer A; Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain.; valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain., Wiedemeyer K; Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada., López-Takegami JC; Grupo de investigación IMPAC, Fundación Universitaria Sanitas, Bogotá, Colombia., Palma LF; Grupo de investigación IMPAC, Fundación Universitaria Sanitas, Bogotá, Colombia., Undersrud E; Department of Pathology, Stavanger University Hospital, Stavanger, Norway., Janssen E; Department of Pathology, Stavanger University Hospital, Stavanger, Norway.; Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway., Brenn T; Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada., Naranjo V; Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain., Monteagudo C; Universitat de València, Valencia, Spain.; INCLIVA, Instituto de Investigación Sanitaria, Valencia, Spain.

Publikováno v: Histopathology [Histopathology] 2024 Jul; Vol. 85 (1), pp. 155-170. Date of Electronic Publication: 2024 Apr 12.

An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images

Autor: del Amor R, Launet L, Colomer A, Moscardo A, Mosquera-Zamudio A, Monteagudo C, Naranjo V

Publikováno v: ARTIFICIAL INTELLIGENCE IN MEDICINE
r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
instname

Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=RECOLECTA___::820ba49b88b7f7a0dfbfc7ee7af8d72f
https://www.fundanet.incliva.es/publicaciones/ProdCientif/PublicacionFrw.aspx?id=16363

Constrained multiple instance learning for ulcerative colitis prediction using histological images.

Autor: Del Amor R; Instituto de Investigación e Innovación en Bioingeniería, Universitat Politécnica de Valéncia, Valencia, Spain. Electronic address: madeam2@upvnet.upv.es., Meseguer P; Instituto de Investigación e Innovación en Bioingeniería, Universitat Politécnica de Valéncia, Valencia, Spain., Parigi TL; Department of Biomedical Sciences, Humanitas University, Milan, Italy; University of Birmingham, Immunology and Immunotherapy, Birmingham, United Kingdom., Villanacci V; Institute of Pathology, ASST Spedali Civili, University of Brescia, Brescia, Italy., Colomer A; Instituto de Investigación e Innovación en Bioingeniería, Universitat Politécnica de Valéncia, Valencia, Spain., Launet L; Instituto de Investigación e Innovación en Bioingeniería, Universitat Politécnica de Valéncia, Valencia, Spain., Bazarova A; Institute for Biological Physics, University of Cologne, Cologne, Germany., Tontini GE; Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy., Bisschops R; Division of Gastroenterology, University Hospitals Leuven, Leuven, Belgium., de Hertogh G; Division of Gastroenterology, University Hospitals Leuven, Leuven, Belgium., Ferraz JG; Division of Gastroenterology, University of Calgary Cumming School of Medicine, Calgary, Canada., Götz M; Division of Gastroenterology, Klinikum, Böblingen, Germany., Gui X; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, USA., Hayee B; Division of Gastroenterology, Kings College London, London, United Kingdom., Lazarev M; Division of Gastroenterology, Johns Hopkins Hospital, Baltimore, United States., Panaccione R; Division of Gastroenterology, University of Calgary Cumming School of Medicine, Calgary, Canada., Parra-Blanco A; Division of Gastroenterology, University of Nottingham, Nottingham, United Kingdom., Bhandari P; Division of Gastroenterology, Queen Alexandra Hospital, Portsmouth, United Kingdom., Pastorelli L; Liver and Gastroenterology Unit, Universita' degli Studi di Milano, ASST Santi Paolo E Carlo, University Hospital San Paolo, Milan, Italy., Rath T; Division of Gastroenterology, University of Erlangen, Erlangen, Germany., Røyset ES; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway., Vieth M; Klinikum Bayreuth, Bayreuth, Germany; Institute of Pathology, Friedrich-Alexander-University Erlangen-Nuremberg, Nuremberg, Germany., Zardo D; Department of Pathology, San Bortolo Hospital, Vicenza, Italy., Grisan E; Department of Information Engineering, Padova, Italy; School of Engineering, London South Bank University, London, UK., Ghosh S; APC Microbiome Ireland, College of Medicine and Health, Cork, Ireland; Institute for Biological Physics, University of Cologne, Cologne, Germany., Iacucci M; Department of Biomedical Sciences, Humanitas University, Milan, Italy; National Institute for Health Research (NIHR) Biomedical Research Centre, Birmingham, United Kingdom; Department of Gastroenterology, University Hospitals Birmingham NHS Trust, Birmingham, United Kingdom., Naranjo V; Instituto de Investigación e Innovación en Bioingeniería, Universitat Politécnica de Valéncia, Valencia, Spain.

Publikováno v: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2022 Sep; Vol. 224, pp. 107012. Date of Electronic Publication: 2022 Jul 09.

Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.

Autor: Maher NG; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia., Danaei Mehr H; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia., Cong C; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia., Adegoke NA; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia., Vergara IA; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia., Liu S; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia., Scolyer RA; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.

Publikováno v: Journal of the European Academy of Dermatology and Venereology : JEADV [J Eur Acad Dermatol Venereol] 2024 Aug 31. Date of Electronic Publication: 2024 Aug 31.

Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer.

Autor: Ayana, Gelan^1,2 (AUTHOR) gelan@kumoh.ac.kr, Barki, Hika³ (AUTHOR) daljuhika@pukyong.ac.kr, Choe, Se-woon^1,4,5 (AUTHOR) musej2s8@ufl.edu

Publikováno v: Cancers. Apr2024, Vol. 16 Issue 7, p1441. 20p.