March 14, 2024, 4:42 a.m. | Amel Imene Hadj Bouzid, Baudouin Denis de Senneville, Fabien Baldacci, Pascal Desbarats, Patrick Berger, Ilyes Benlala, Ga\"el Dournes

cs.LG updates on

arXiv:2403.08042v1 Announce Type: cross
Abstract: This research embarked on a comparative exploration of the holistic segmentation capabilities of Convolutional Neural Networks (CNNs) in both 2D and 3D formats, focusing on cystic fibrosis (CF) lesions. The study utilized data from two CF reference centers, covering five major CF structural changes. Initially, it compared the 2D and 3D models, highlighting the 3D model's superior capability in capturing complex features like mucus plugs and consolidations. To improve the 2D model's performance, a loss …

abstract arxiv capabilities cnns convolutional neural networks cs.lg data deep learning eess.iv evaluation exploration networks neural networks reference research segmentation study type

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