19 citations · 25 across the 7 of their papers we have counts for
7 papers
VesselBridge3D: A Foundation Model Adaptation Framework for Label-Efficient 3D Vessel Segmentation
Kirato Yoshihara, Yohei Sugawara, Yuta Tokuoka +1
State-of-the-art vessel segmentation methods typically require large-scale annotated datasets and suffer from severe performance degradation under domain shifts. In clinical practi…
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
Kenta Oono, Nontawat Charoenphakdee, Kotatsu Bito +14
Virtual Human Generative Model (VHGM) is a generative model that approximates the joint probability over more than 2000 human healthcare-related attributes. This paper presents the…
Label-Efficient Multi-Task Segmentation using Contrastive Learning
Junichiro Iwasawa, Yuichiro Hirano, Yohei Sugawara
Obtaining annotations for 3D medical images is expensive and time-consuming, despite its importance for automating segmentation tasks. Although multi-task learning is considered an…
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation
Yuta Tokuoka, Shuji Suzuki, Yohei Sugawara
With recent advances in supervised machine learning for medical image analysis applications, the annotated medical image datasets of various domains are being shared extensively. G…
GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention
Ashish Sinha, Yohei Sugawara, Yuichiro Hirano
This work investigates the use of guided attention in the reconstruction of CTvolumes from biplanar DRRs. We try to improve the visual image quality of the CT reconstruction using…
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara +1
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP…
BayesGrad: Explaining Predictions of Graph Convolutional Networks
Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu +4
Recent advances in graph convolutional networks have significantly improved the performance of chemical predictions, raising a new research question: "how do we explain the predict…