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

Updated: Dec 21, 2021

Oct. 2021

aetherAI presented a machine learning approach for accurate quantification of nuclear morphometrics and differential diagnosis of primary intestinal T-cell lymphomas. The human interpretable machine learning approach can be easily applied to other lymphomas and potentially even broader disease categories. This approach not only brings deeper insights into lymphoma phenotypes but also paves the way for future discoveries concerning their relationship with disease classification and outcome.

This study has collaborated with Dr. Shih-Sung Chuang, Department of Pathology, Chi-Mei Medical Center, and 4 other departments of Pathology. The study, < Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas> is accepted and published by Cancers, a journal of oncology in Oct. 2021.

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雲象AI科學家運用數千顆細胞核的標註資料,訓練出的AI模型能對淋巴瘤的細胞核精確地偵測,描繪輪廓,並且進一步計算出每個細胞核面積大小,長短軸比例等可量化的形態資訊,依此進一步訓練機器學習演算法,對MEITL以及ITCL-NOS進行分類,預測水準可高達AUC 96.6%。