no code implementations • 8 Dec 2023 • Akimichi Ichinose, Taro Hatsutani, Keigo Nakamura, Yoshiro Kitamura, Satoshi Iizuka, Edgar Simo-Serra, Shoji Kido, Noriyuki Tomiyama
Our framework combines two components of 1) anatomical segmentation of images, and 2) report structuring.
no code implementations • 8 Dec 2023 • Saeko Sasuga, Akira Kudo, Yoshiro Kitamura, Satoshi Iizuka, Edgar Simo-Serra, Atsushi Hamabe, Masayuki Ishii, Ichiro Takemasa
To tackle this, we propose two kinds of approaches of image synthesis-based late stage cancer augmentation and semi-supervised learning which is designed for T-stage prediction.
no code implementations • 26 Sep 2023 • Guoqing Hao, Satoshi Iizuka, Kensho Hara, Edgar Simo-Serra, Hirokatsu Kataoka, Kazuhiro Fukui
We present a novel framework for rectifying occlusions and distortions in degraded texture samples from natural images.
no code implementations • 19 Aug 2023 • Yuantian Huang, Satoshi Iizuka, Edgar Simo-Serra, Kazuhiro Fukui
To address this problem, we propose a dataset, which we call ArtSem, that contains 40, 000 images of artwork from 4 different domains with their corresponding semantic label maps.
no code implementations • 1 Dec 2022 • Yutaka Momma, Weimin WANG, Edgar Simo-Serra, Satoshi Iizuka, Ryosuke Nakamura, Hiroshi Ishikawa
To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method.
1 code implementation • 26 Jul 2022 • Tomoki Uchiyama, Naoya Sogi, Satoshi Iizuka, Koichiro Niinuma, Kazuhiro Fukui
The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporal-spatial data space and then measure the change degree in the output score.
no code implementations • 29 Sep 2020 • Naoto Masuzawa, Yoshiro Kitamura, Keigo Nakamura, Satoshi Iizuka, Edgar Simo-Serra
The input to the second networks have an auxiliary channel in addition to the 3D CT images.
no code implementations • 18 Sep 2020 • Deepak Keshwani, Yoshiro Kitamura, Satoshi Ihara, Satoshi Iizuka, Edgar Simo-Serra
To the best of our knowledge, this is the first deep learning based approach which learns multi-label tree structure connectivity from images.
no code implementations • 18 Sep 2020 • 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 medium to its original state.
2 code implementations • 25 Mar 2020 • Shuhei Yokoo, Kohei Ozaki, Edgar Simo-Serra, Satoshi Iizuka
Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity.
no code implementations • 30 Aug 2019 • Akira Kudo, Yoshiro Kitamura, Yuanzhong Li, Satoshi Iizuka, Edgar Simo-Serra
In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs.
no code implementations • CVPR 2017 • Kazuma Sasaki, Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa
We evaluate our method qualitatively on a diverse set of challenging line drawings and also provide quantitative results with a user study, where it significantly outperforms the state of the art.
no code implementations • 27 Mar 2017 • Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa
Our approach augments a simplification network with a discriminator network, training both networks jointly so that the discriminator network discerns whether a line drawing is a real training data or the output of the simplification network, which in turn tries to fool it.
3 code implementations • ACM Transactions on Graphics 2016 • Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa
We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.