14 citations · 27 across the 11 of their papers we have counts for
12 papers
Visual Grounding of Whole Radiology Reports for 3D CT Images
Akimichi Ichinose, Taro Hatsutani, Keigo Nakamura +5
Building a large-scale training dataset is an essential problem in the development of medical image recognition systems. Visual grounding techniques, which automatically associate…
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging
Saeko Sasuga, Akira Kudo, Yoshiro Kitamura +5
Rectal cancer is one of the most common diseases and a major cause of mortality. For deciding rectal cancer treatment plans, T-staging is important. However, evaluating the index f…
Diffusion-based Holistic Texture Rectification and Synthesis
Guoqing Hao, Satoshi Iizuka, Kensho Hara +3
We present a novel framework for rectifying occlusions and distortions in degraded texture samples from natural images. Traditional texture synthesis approaches focus on generating…
Controllable Multi-domain Semantic Artwork Synthesis
Yuantian Huang, Satoshi Iizuka, Edgar Simo-Serra +1
We present a novel framework for multi-domain synthesis of artwork from semantic layouts. One of the main limitations of this challenging task is the lack of publicly available seg…
P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames
Yutaka Momma, Weimin Wang, Edgar Simo-Serra +3
We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encou…
Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
Tomoki Uchiyama, Naoya Sogi, Satoshi Iizuka +2
This paper proposes a method for visually explaining the decision-making process of video recognition networks with a temporal extension of occlusion sensitivity analysis, called A…
Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks
Naoto Masuzawa, Yoshiro Kitamura, Keigo Nakamura +2
This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3D CT images. Many previous works do not perform the three tasks…
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement
Satoshi Iizuka, Edgar Simo-Serra
The remastering of vintage film comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which aim to restore the deteriorated film…
TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling
Deepak Keshwani, Yoshiro Kitamura, Satoshi Ihara +2
Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning base…
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
Shuhei Yokoo, Kohei Ozaki, Edgar Simo-Serra +1
We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is b…
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval
Akira Kudo, Yoshiro Kitamura, Yuanzhong Li +2
Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis…
Mastering Sketching: Adversarial Augmentation for Structured Prediction
Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa
We present an integral framework for training sketch simplification networks that convert challenging rough sketches into clean line drawings. Our approach augments a simplificatio…