Visualizing Association in Exemplar-Based Classification

Autor: Ryuichiro Hataya, Taiga Kashima, Hideki Nakayama

Publikováno v: ICASSP

Recent progress in deep learning has enhanced image classification performance. However, classification using deep convolutional neural networks lacks interpretability. To solve this problem, we propose a novel method of explainable classification; t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::12932017884edbf99eb6ad6f2ad732d3
https://doi.org/10.1109/icassp39728.2021.9413574

CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

Autor: Yudai Nagano, Claudio Ferretti, Carmelo Militello, Leonardo Rundo, Ryuichiro Hataya, Salvatore Vitabile, Giancarlo Mauri, Marco S. Nobile, Changhee Han, Maria Carla Gilardi, Jin Zhang, Andrea Tangherloni, Hideki Nakayama

Publikováno v: Neural Approaches to Dynamics of Signal Exchanges, edited by Anna Esposito, Marcos Faundez-Zanuy Francesco Carlo Morabito, Eros Pasero, pp. 269–280. Basel: Springer Nature Switzerland, 2020
info:cnr-pdr/source/autori:Rundo L.; Han C.; Zhang J.; Hataya R.; Nagano Y.; Militello C.; Ferretti C.; Nobile M.S.; Tangherloni A.; Gilardi M.C.; Vitabile S.; Nakayama H.; Mauri G./titolo:CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study/titolo_volume:Neural Approaches to Dynamics of Signal Exchanges/curatori_volume:Anna Esposito, Marcos Faundez-Zanuy Francesco Carlo Morabito, Eros Pasero,/editore: /anno:2020
Neural Approaches to Dynamics of Signal Exchanges ISBN: 9789811389498
Neural Approaches to Dynamics of Signal Exchanges

Prostate canceris the most common cancer among US men. However, prostateimaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7806e6c99f6f4d16f227155e7434fb5b
http://hdl.handle.net/10447/418700

Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging

Autor: Tatsuya Harada, Ryuichiro Hataya, Masamichi Takahashi, Ryuji Hamamoto, Akiko Nakagawa, Kazuma Kobayashi, Mototaka Miyake, Yusuke Kurose

Publikováno v: Medical Image Analysis. 74:102227

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 often need corresponding images without abnormal findings of interest or,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61ab41233bdd9a4e299a147ece6362c2
https://doi.org/10.1016/j.media.2021.102227

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Autor: Claudio Ferretti, Carmelo Militello, Yudai Nagano, Changhee Han, Hideki Nakayama, Paolo Cazzaniga, Giancarlo Mauri, Salvatore Vitabile, Jin Zhang, Maria Carla Gilardi, Ryuichiro Hataya, Marco S. Nobile, Andrea Tangherloni, Daniela Besozzi, Leonardo Rundo

Publikováno v: Neurocomputing (Amst.) 365 (2019): 31–43. doi:10.1016/j.neucom.2019.07.006
info:cnr-pdr/source/autori:Rundo, Leonardo*; Han, Changhee*; Nagano, Yudai; Zhang, Jin; Hataya, Ryuichiro; Militello, Carmelo; Tangherloni, Andrea; Nobile, Marco S.; Ferretti, Claudio; Besozzi, Daniela; Gilardi, Maria Carla; Vitabile, Salvatore; Mauri, Giancarlo; Nakayama, Hideki; Cazzaniga, Paolo/titolo:USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets/doi:10.1016%2Fj.neucom.2019.07.006/rivista:Neurocomputing (Amst.)/anno:2019/pagina_da:31/pagina_a:43/intervallo_pagine:31–43/volume:365

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Centr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a61985bdd1937c350efeb9c8325061e
http://hdl.handle.net/10447/391677