Have you ever collect data with google sheets where the sheets are shared with everyone in your organization for inputting their weekly, monthly data such as performance reporting, inventory…
Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value. Recent advances in machine learning techniques have created opportunities to improve medical diagnostics, but implementing these advances in the clinic will not be without challenge.
Read the latest articles of Diagnostic Histopathology at ScienceDirect.com, Elsevier’s leading platform of peer-reviewed scholarly literature
Eski usül basılmış radyolojik görüntüleri tekrar devasa scannerlar ile tarayıp dijitalleştirip adına dijital radyoloji denseydi nasıl sorunlar yaşanırdı acaba? Tanıya hazır hale gelmiş preparatı tekrar tanıya hazır hale getirmek için dijital patolojide uğraştığımız gibi mi olurdu?
Skorlamaları zorlayarak “hastanın tedavi alma şansına engel olmamak” akımı tababetin “primum non nocere” ilkesine aykırı. İlminin gereğince amel edenlere Aşk olsun.