@article{uninimx15623,
            year = {2024},
          volume = {24},
           title = {Virtual histopathology methods in medical imaging - a systematic review},
          author = {Muhammad Talha Imran and Imran Shafi and Jamil Ahmad and Muhammad Fasih Uddin Butt and Santos Gracia Villar and Eduardo Garc{\'i}a Villena and Tahir Khurshaid and Imran Ashraf},
          number = {1},
         journal = {BMC Medical Imaging},
           month = {Noviembre},
        keywords = {Dual contrastive learning, Image-to-image translation, Virtual histopathology, Medical image processing,
Computational pathology},
             url = {http://repositorio.unini.edu.mx/id/eprint/15623/},
        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.}
}