7 citations · 22 across the 10 of their papers we have counts for
10 papers
NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results
Shuhong Liu, Chenyu Bao, Ziteng Cui +103
This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to…
RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction
Shuhong Liu, Chenyu Bao, Ziteng Cui +11
We introduce RealX3D, a real-capture benchmark for multi-view visual restoration and 3D reconstruction under diverse physical degradations. RealX3D groups corruptions into four fam…
Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images
Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta +7
Pathological image analysis is an important process for detecting abnormalities such as cancer from cell images. However, since the image size is generally very large, the cost of…
Sketch-based Medical Image Retrieval
Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya +12
The amount of medical images stored in hospitals is increasing faster than ever; however, utilizing the accumulated medical images has been limited. This is because existing conten…
Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall
Antonio Tejero-de-Pablos, Hiroaki Yamane, Yusuke Kurose +7
The presence of plaques in the coronary arteries is a major risk to the patients' life. In particular, non-calcified plaques pose a great challenge, as they are harder to detect an…
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval
Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose +5
In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians ofte…
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval
Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose +2
Medical images can be decomposed into normal and abnormal features, which is considered as the compositionality. Based on this idea, we propose an encoder-decoder network to decomp…
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection
Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose +7
In real-world clinical practice, overlooking unanticipated findings can result in serious consequences. However, supervised learning, which is the foundation for the current succes…
Coronary Wall Segmentation in CCTA Scans via a Hybrid Net with Contours Regularization
Kaikai Huang, Antonio Tejero-de-Pablos, Hiroaki Yamane +8
Providing closed and well-connected boundaries of coronary artery is essential to assist cardiologists in the diagnosis of coronary artery disease (CAD). Recently, several deep lea…
Multi-Stage Pathological Image Classification using Semantic Segmentation
Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta +5
Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigap…