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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.1
- 수록면
- 92 - 92 (1page)
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In recent years, the development of artificial intelligence (deep learning) technology has been accelerating the attempt to detect pathological lesions automatically from the whole slide image (WSI) in the clinical area. Artificial intelligence could also be used for toxico-pathological diagnosis in the non-clinical study. We conducted deep learning-based instance segmentation using Mask R-CNN to detect pathological findings, such as fibrosis in SD rats which has been the well-known animal model in the toxicological area, for diagnostic aid. Total 3,489 images with fibrosis lesions were labeled by VGG annotator 2.0.1.0. All the procedures were conducted using the AI method by Tensorflow 2.1.0 powered by NVIDIA RTX 2080ti 11G GPU, and the model performance for prediction was determined by the mean average precision (mAP). The mAP can be inferred from the intersection of union (IoU) that measures^_@span style=color:#999999 ^_# ... ^_@/span^_#^_@a href=javascript:; onclick=onClickReadNode('NODE10540230');fn_statistics('Z354','null','null'); style='color:#999999;font-size:14px;text-decoration:underline;' ^_#전체 초록 보기^_@/a^_#
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UCI(KEPA) : I410-ECN-0101-2021-528-001569741