1 code implementation • 22 Nov 2024 • Kaito Shiku, Kazuya Nishimura, Daiki Suehiro, Kiyohito Tanaka, Ryoma Bise
Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded.
1 code implementation • 29 Oct 2024 • Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida
By appropriately using the soft and hard labels in the two techniques, we achieve more accurate sample selection and robust network training.
no code implementations • 8 Sep 2024 • Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida
In general, severity estimation uses training data annotated with discrete (i. e., quantized) severity labels.
no code implementations • 24 Feb 2023 • Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida, Takeaki Kadota
We propose continuous DA as a solution to the two issues.
no code implementations • 5 Aug 2022 • Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida
This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation.
no code implementations • 13 Jan 2022 • Masahiro Oda, Kiyohito Tanaka, Hirotsugu TAKABATAKE, Masaki MORI, Hiroshi NATORI, Kensaku MORI
Virtual endoscopic images are generated by using a volume rendering method from a CT volume of a patient.
no code implementations • 12 Jan 2022 • Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu TAKABATAKE, Masaki MORI, Hiroshi NATORI, Kensaku MORI
The identification accuracy of the network improved from 69. 2% to 74. 1% by using the estimated depth images.
no code implementations • 6 Nov 2021 • Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties.