Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping

no code implementations • 27 Mar 2023 • Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku MORI

This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes.

Specificity

COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty

no code implementations • 9 Jan 2022 • Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku MORI

We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.

Segmentation

The utility of a convolutional neural network for generating a myelin volume index map from rapid simultaneous relaxometry imaging

no code implementations • 24 Apr 2019 • Yasuhiko Tachibana, Akifumi Hagiwara, Masaaki Hori, Jeff Kershaw, Misaki Nakazawa, Tokuhiko Omatsu, Riwa Kishimoto, Kazumasa Yokoyama, Nobutaka Hattori, Shigeki Aoki, Tatsuya Higashi, Takayuki Obata

A CNN was trained to reconstruct RSRI-related metric maps into a myelin volume-related index (generated myelin volume index: GenMVI) map using the myelin volume index map calculated from magnetization transfer images (MTMVI) as reference.