aetherAI’s initiative in a whole-slide training approach is globally recognized

Updated: Dec 17, 2021

Feb. 2021 published by Commercial Times

The article, An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning in the international journal, co-authored by Taipei Medical University and aetherAI was published by Nature Communications. It recognizes the proposed method breakthrough, which is ahead of other well-known AI brands, and solves pain points in digital pathology AI development.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, it develops a method for training neural networks on entire WSIs using only slide-level diagnoses.

Fig. 4, both the MIL model and whole-slide model could discover representative information, which was highlighted by heatmaps after iteratively learning from slide-level diagnosis.

Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping. It is expected that this proposed method can realize AI applications empowered by digital pathology development.

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雲象科技今日發表與臺北醫學大學合作的數位病理AI共同研究成果登上Nature Communications,此突破性的研究採用零細節標註零分割方法,得以將未經切割的病理全玻片影像直接用於訓練AI模型,大幅節省醫師專業人力,技術超越美國知名病理AI公司Paige,成功解決數位病理AI開發痛點。