Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning–based Radiograph Diagnosis: A Multicenter Study
24
Zitationen
13
Autoren
2022
Jahr
Abstract
Fine-grained annotations overcame shortcut learning and enabled DL models to identify correct lesion patterns, improving the generalizability of the models.<b>Keywords:</b> Computer-aided Diagnosis, Conventional Radiography, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Localization <i>Supplemental material is available for this article</i> © RSNA, 2022.
Ähnliche Arbeiten
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
2020 · 22.607 Zit.
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
2020 · 19.271 Zit.
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
2024 · 14.251 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.479 Zit.
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021 · 7.095 Zit.
Autoren
Institutionen
- Chinese University of Hong Kong(CN)
- Chinese University of Hong Kong, Shenzhen(CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College(CN)
- Hong Kong University of Science and Technology(HK)
- University of Hong Kong(HK)
- Shenzhen Luohu People's Hospital(CN)
- Guangdong Provincial People's Hospital(CN)
- Guangdong Academy of Medical Sciences(CN)
- Queen Mary Hospital(CN)
- Hospital Authority(CN)
- Shenzhen Institutes of Advanced Technology(CN)