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.