5 citations · 5 across the 4 of their papers we have counts for
4 papers
ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising
Xuepeng Liu, Zetong Liu, Renyiming Li +5
Low-dose computed tomography (LDCT) reduces radiation exposure but introduces stronger quantum noise, streak artifacts, and local texture degradation, which can obscure anatomical…
ModAn-MulSupCon: Modality-and Anatomy-Aware Multi-Label Supervised Contrastive Pretraining for Medical Imaging
Eichi Takaya, Ryusei Inamori
Background and objective: Expert annotations limit large-scale supervised pretraining in medical imaging, while ubiquitous metadata (modality, anatomical region) remain underused.…
In-context learning for medical image segmentation
Eichi Takaya, Shinnosuke Yamamoto
Annotation of medical images, such as MRI and CT scans, is crucial for evaluating treatment efficacy and planning radiotherapy. However, the extensive workload of medical professio…
PlaNet-S: Automatic Semantic Segmentation of Placenta
Shinnosuke Yamamoto, Isso Saito, Eichi Takaya +5
[Purpose] To develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. [Methods] A total of 218…