22 citations · 36 across the 9 of their papers we have counts for
10 papers
Quasi-Binarized Autoencoders: An Architecture-Independent Information Bottleneck for Medical Image Anomaly Detection
Shouhei Hanaoka, Takahiro Nakao, Atsushi Takamatsu +2
Unsupervised anomaly detection, which learns only from normal images, is a central task in medical image analysis and remains an open problem. Reconstruction-based methods pass an…
CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models
Ana Lawry Aguila, Ayodeji Ijishakin, Juan Eugenio Iglesias +5
Applying machine learning to real-world medical data, e.g. from hospital archives, has the potential to revolutionize disease detection in brain images. However, detecting patholog…
ModernBERT is More Efficient than Conventional BERT for Chest CT Findings Classification in Japanese Radiology Reports
Yosuke Yamagishi, Tomohiro Kikuchi, Shouhei Hanaoka +2
Japanese language models for medical text classification face challenges with complex vocabulary and linguistic structures in radiology reports. This study compared three Japanese…
Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model
Yosuke Yamagishi, Yuta Nakamura, Tomohiro Kikuchi +7
Background: Recent advances in large language models highlight the need for high-quality multilingual medical datasets. While Japan leads globally in CT scanner deployment and util…
Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging
Yosuke Yamagishi, Shouhei Hanaoka, Tomohiro Kikuchi +6
Objectives: To evaluate the zero-shot performance of Segment Anything Model 2 (SAM 2) in 3D segmentation of abdominal organs in CT scans, and to investigate the effects of prompt s…
Data Set Terminology of Deep Learning in Medicine: A Historical Review and Recommendation
Shannon L. Walston, Hiroshi Seki, Hirotaka Takita +7
Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of ter…
Method for Generating Synthetic Data Combining Chest Radiography Images with Tabular Clinical Information Using Dual Generative Models
Tomohiro Kikuchi, Shouhei Hanaoka, Takahiro Nakao +4
The generation of synthetic medical records using Generative Adversarial Networks (GANs) is becoming crucial for addressing privacy concerns and facilitating data sharing in the me…
X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols
Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura +4
Ultra sparse-view computed tomography (CT) algorithms can reduce radiation exposure of patients, but those algorithms lack an explicit cycle consistency loss minimization and an ex…
KART: Parameterization of Privacy Leakage Scenarios from Pre-trained Language Models
Yuta Nakamura, Shouhei Hanaoka, Yukihiro Nomura +5
For the safe sharing pre-trained language models, no guidelines exist at present owing to the difficulty in estimating the upper bound of the risk of privacy leakage. One problem i…
On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks
Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura +2
In general, adversarial perturbations superimposed on inputs are realistic threats for a deep neural network (DNN). In this paper, we propose a practical generation method of such…