eprintid: 15623 rev_number: 10 eprint_status: archive userid: 2 dir: disk0/00/01/56/23 datestamp: 2024-12-12 23:30:08 lastmod: 2024-12-12 23:30:09 status_changed: 2024-12-12 23:30:08 type: article metadata_visibility: show creators_name: Imran, Muhammad Talha creators_name: Shafi, Imran creators_name: Ahmad, Jamil creators_name: Butt, Muhammad Fasih Uddin creators_name: Gracia Villar, Santos creators_name: García Villena, Eduardo creators_name: Khurshaid, Tahir creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: santos.gracia@uneatlantico.es creators_id: eduardo.garcia@uneatlantico.es creators_id: creators_id: title: Virtual histopathology methods in medical imaging - a systematic review ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Dual contrastive learning, Image-to-image translation, Virtual histopathology, Medical image processing, Computational pathology abstract: Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings. date: 2024-11 publication: BMC Medical Imaging volume: 24 number: 1 id_number: doi:10.1186/s12880-024-01498-9 refereed: TRUE issn: 1471-2342 official_url: http://doi.org/10.1186/s12880-024-01498-9 access: open language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings. metadata Imran, Muhammad Talha; Shafi, Imran; Ahmad, Jamil; Butt, Muhammad Fasih Uddin; Gracia Villar, Santos; García Villena, Eduardo; Khurshaid, Tahir y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, eduardo.garcia@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Virtual histopathology methods in medical imaging - a systematic review. BMC Medical Imaging, 24 (1). ISSN 1471-2342 document_url: http://repositorio.unib.org/id/eprint/15623/1/s12880-024-01498-9.pdf