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aetherAI’s Machine Learning Approach for T-cell Lymphomas Diagnosis Support published in Cancers

Published by Cancers, Oct. 2021 aetherAI presented a machine learning approach for accurate quantification of nuclear morphometrics and...

Published by Biomedical Journal, Sept. 2021 Abstract Background Classification of glomerular diseases and identification of glomerular...

Published by MICCAI2021, Sept. 2021 Abstract Computational histopathology studies have shown that stain color variations considerably...

Published by MICCAI2021, Sept. 2021 Abstract Understanding of prognosis and mortality is crucial for evaluating the treatment plans for...

Published by Modern Pathology, June 2021 Abstract Detection of nodal micrometastasis (tumor size: 0.2–2.0 mm) is challenging for...

Published by Scientific Report, April 2021 Deep learning approach for automatic landmark detection and alignment analysis in whole-spine...

Published by American Journal of Clinical Pathology, Feb. 2021 Abstract Objectives This study aimed to develop and validate a deep...

Published by Nature Communications, Feb. 2021 Abstract Deep learning for digital pathology is hindered by the extremely high spatial...

Published by Annals of Translational Medicine, Jan. 2021 Predicting aggressive histopathological features in esophageal cancer with...

Published by Cancers, Feb. 2020 Abstract Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are...

Published by Journal of Clinical Medicine on 13 June 2019 Abstract In esophageal cancer, few prediction tools can be confidently used in...