English(EN) Toxicity Assessment in Preclinical Histopathology via Class-Aware Mahalanobis Distance for Known and Novel Anomalies

AI利用新发异常检测技术检测临床前组织病理学中的毒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-01 04:00

研究人员开发了一个AI框架，利用全切片图像检测临床前组织病理学中的毒性。该系统可以识别健康组织、已知病理，并标记出具有新发异常的样本。通过使用低秩自适应微调Vision Transformer并采用马氏距离进行异常检测，该方法旨在提高药物开发中毒性评估的效率和规模。 AI

影响通过自动化组织病理学毒性评估，可能加速临床前药物开发。

排序理由研究论文，详细介绍了用于组织病理学毒性评估的新型AI方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Olga Graf, Dhrupal Patel, Peter Gro{\ss}, Charlotte Lempp, Matthias Hein, Fabian Heinemann · 2026-07-01 04:00

通过类感知马氏距离对已知和新颖异常进行临床前组织病理学毒性评估

arXiv:2602.02124v2 Announce Type: replace-cross Abstract: Drug-induced toxicity is a leading cause of preclinical and early-clinical failure, making early detection critical. Histopathology is the gold standard for toxicity assessment but relies on expert pathologists, creating a…