Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation

Autor:	Joohyun Lee, Dongmyung Shin, Se-Hong Oh, Haejin Kim
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	brain tumor semantic segmentation uncertainty quantification attention mechanism Chemical technology TP1-1185
Zdroj:	Sensors, Vol 22, Iss 6, p 2406 (2022)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s22062406
Popis:	Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In this study, we proposed a reliable deep learning framework that could minimize incorrect segmentation by quantifying and exploiting uncertainty measures. The proposed framework demonstrated the effectiveness of a public dataset: Multimodal Brain Tumor Segmentation Challenge 2018. By using this framework, segmentation performances, particularly for small lesions, were improved. Since the segmentation of small lesions is difficult but also clinically significant, this framework could be effectively applied to the medical imaging field.
Databáze:	Directory of Open Access Journals
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