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aetherAI developed a method for training neural networks on whole WSIs using just slide-level diagnosis to relieve the load of such contouring and reap the benefits of scaling up training with many WSIs. On WSIs, this usually entails tedious free-hand contouring. Patch-based approaches have been used in most research, which frequently needs comprehensive annotation of picture patches. The extraordinarily high spatial resolution of whole-slide pictures makes deep learning for digital pathology difficult (WSIs).
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For the first time, companies’ work shows that micrometastasis may be recognized using deep learning on whole-slide photos without manual annotation. With areas under the receiver operating characteristics curve (AUC) of 0.9993 and 0.9956, respectively, the aetherAI algorithm performed well in detecting macrometastasis and micrometastasis at the slide level. “Deep learning on annotation-free whole-slide images (WSI) was used to identify nodal micrometastasis in colorectal cancer” was published in the peer-reviewed Modern Pathology by aetherAI, Asia’s top medical image solution provider specialized in digital pathology and medical imaging AI. Please Don't Forget To Join Our ML Subreddit
All Credit For This Research Goes To The Researchers of This Project.
This Article is written as a summay by Marktechpost Staff based on the research paper ' Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images '.
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