480 citations · 506 across the 9 of their papers we have counts for
9 papers
Synthesizing 3D computed tomography from MRI or CBCT using 2.5D deep neural networks
Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa
Deep learning techniques, particularly convolutional neural networks (CNNs), have gained traction for synthetic computed tomography (sCT) generation from Magnetic resonance imaging…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
Automated Lesion Segmentation in Whole-Body FDG-PET/CT with Multi-modality Deep Neural Networks
Satoshi Kondo, Satoshi Kasai
Recent progress in automated PET/CT lesion segmentation using deep learning methods has demonstrated the feasibility of this task. However, tumor lesion detection and segmentation…
Unsupervised Domain Adaptation for MRI Volume Segmentation and Classification Using Image-to-Image Translation
Satoshi Kondo, Satoshi Kasai
Unsupervised domain adaptation is a type of domain adaptation and exploits labeled data from the source domain and unlabeled data from the target one. In the Cross-Modality Domain…
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Coen de Vente, Koenraad A. Vermeer, Nicolas Jaccard +33
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effectiv…
Source-Free Unsupervised Domain Adaptation with Norm and Shape Constraints for Medical Image Segmentation
Satoshi Kondo
Unsupervised domain adaptation (UDA) is one of the key technologies to solve a problem where it is hard to obtain ground truth labels needed for supervised learning. In general, UD…