Pathology is a branch of medical science primarily concerning the examination of organs, tissues, and bodily fluids in order to make a diagnosis of disease. It is used extensively in forensic investigations, autopsy, diagnosis as well as in education and training. Digital pathology is an image-based information environment which is enabled by computer technology that allows for the management of information generated from digital slides. With the advent of whole-slide imaging as well as the development of Machine Learning and Artificial Intelligence, the field of digital pathology has gained considerable interest and is currently regarded as one of the most promising avenues of diagnostic medicine in order to achieve even better, faster and cheaper diagnosis, prognosis and prediction of cancer and other important diseases.
This Research Topic provides a forum for the presentation and discussion on the different feature extractions and Deep Learning approaches developed for digital pathology applications. These range from single applications such as tumor detection, tissue/cell segmentation, classification, cell counting, cell scoring etc. to more complex applications such as tumor grading, cancer staging, content- and context-based retrieval system, prognostic reporting systems etc. Other related applications such as acquisition, image registrations, labeling/annotations, real-time imaging etc. are also considered. The feature extraction and Deep Learning systems can be for single or multiple cancers (bone, brain, breast, cervix, colorectal, gastric, kidney, lung, pancreas, prostate gland, skin etc.), as well as other diseases.
We accept submissions reporting technical description of feature extraction and/or Deep Learning approaches in digital pathology. The scope of digital pathology includes, but not limited to:
• Any digital pathology applications
• Any pathology stains (H&E, IHC, ISH etc.)
• Any pathology branches (cytopathology, histopathology etc.)
• Single or multiple cancers or other diseases
Pathology is a branch of medical science primarily concerning the examination of organs, tissues, and bodily fluids in order to make a diagnosis of disease. It is used extensively in forensic investigations, autopsy, diagnosis as well as in education and training. Digital pathology is an image-based information environment which is enabled by computer technology that allows for the management of information generated from digital slides. With the advent of whole-slide imaging as well as the development of Machine Learning and Artificial Intelligence, the field of digital pathology has gained considerable interest and is currently regarded as one of the most promising avenues of diagnostic medicine in order to achieve even better, faster and cheaper diagnosis, prognosis and prediction of cancer and other important diseases.
This Research Topic provides a forum for the presentation and discussion on the different feature extractions and Deep Learning approaches developed for digital pathology applications. These range from single applications such as tumor detection, tissue/cell segmentation, classification, cell counting, cell scoring etc. to more complex applications such as tumor grading, cancer staging, content- and context-based retrieval system, prognostic reporting systems etc. Other related applications such as acquisition, image registrations, labeling/annotations, real-time imaging etc. are also considered. The feature extraction and Deep Learning systems can be for single or multiple cancers (bone, brain, breast, cervix, colorectal, gastric, kidney, lung, pancreas, prostate gland, skin etc.), as well as other diseases.
We accept submissions reporting technical description of feature extraction and/or Deep Learning approaches in digital pathology. The scope of digital pathology includes, but not limited to:
• Any digital pathology applications
• Any pathology stains (H&E, IHC, ISH etc.)
• Any pathology branches (cytopathology, histopathology etc.)
• Single or multiple cancers or other diseases
