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HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening.
Pataki, Bálint Ármin; Olar, Alex; Ribli, Dezso; Pesti, Adrián; Kontsek, Endre; Gyöngyösi, Benedek; Bilecz, Ágnes; Kovács, Tekla; Kovács, Kristóf Attila; Kramer, Zsófia; Kiss, András; Szócska, Miklós; Pollner, Péter; Csabai, István.
Afiliación
  • Pataki BÁ; Department of Physics of Complex Systems, ELTE, Eötvös Loránd University, Budapest, Hungary.
  • Olar A; Department of Physics of Complex Systems, ELTE, Eötvös Loránd University, Budapest, Hungary.
  • Ribli D; Department of Physics of Complex Systems, ELTE, Eötvös Loránd University, Budapest, Hungary.
  • Pesti A; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Kontsek E; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Gyöngyösi B; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Bilecz Á; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Kovács T; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Kovács KA; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Kramer Z; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Kiss A; 2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
  • Szócska M; Health Services Management Training Centre, Semmelweis University, Budapest, Hungary.
  • Pollner P; Health Services Management Training Centre, Semmelweis University, Budapest, Hungary. pollner@angel.elte.hu.
  • Csabai I; MTA-ELTE Statistical and Biological Physics Research Group at Dept. of Biological Physics, Eötvös Loránd University, Budapest, Hungary. pollner@angel.elte.hu.
Sci Data ; 9(1): 370, 2022 06 28.
Article en En | MEDLINE | ID: mdl-35764660
Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from tissue taken from biopsy. This is a time-consuming process. A reliable decision support system would assist healthcare systems that often suffer from a shortage of pathologists. Recent advances in digital pathology allow for high-resolution digitalization of pathological slides. Digital slide scanners combined with modern computer vision models, such as convolutional neural networks, can help pathologists in their everyday work, resulting in shortened diagnosis times. In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. Alongside the whole-slide images, detailed region level annotations are also provided for ten relevant pathological classes. The 200 digital slides, after pre-processing, resulted in 101,389 patches. A single patch is a 512 × 512 pixel image, covering 248 × 248 µm2 tissue area. Versions at higher resolution are available as well. Hopefully, HunCRC, this widely accessible dataset will aid future colorectal cancer computer-aided diagnosis and research.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies / Screening_studies Límite: Humans Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article País de afiliación: Hungria Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies / Screening_studies Límite: Humans Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article País de afiliación: Hungria Pais de publicación: Reino Unido