ARTIFICIAL intelligenceDIGITAL healthDIGITAL diagnostic imagingMETA-analysisDESCRIPTIVE statisticsCLINICAL pathologyEnsuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over...
novel imaging systems and whole slide image (WSI) scanners that have enabled the transition of pathology into the digital era, also known as digital pathology. Within minutes, WSI scanners capture multiple images of entire tissue sections on the slide...
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new...
In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digit...
AI+数字化病理图片用于肿瘤免疫 : Artificial intelligence and digital pathology: Opportunities and implications ... 一、肿瘤免疫治疗biomarker PD-L1 2.TMB 3. MSI 4.复合标志物优于单个标志物,但也需考虑成本和复杂度及可重复性 5. TIL: 不单跟免疫疗效相关,还有跟预后相关的文献是更早的一批。但早期的...
Digital technology has comprehensively transformed the diagnostic methods of medical pathology, upgrading the old model that has, for centuries , used microscopes as the primary tool of observation. Now, digital pathology — already widespread in pathology diagnosis, pathology Artificial Intelligence (AI)...
Scientists from Dana-Farber Cancer Institute and Weill Cornell Medicine have developed and tested new artificial intelligence tools tailored to digital pathology—a relatively new field that uses high-resolution digital images that are created from tissu
Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1–3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important
Digital pathology (DP) has become a part of the cancer healthcare system, creating additional value for cancer patients. DP implementation in
The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we ...