12 citations · 23 across the 5 of their papers we have counts for
5 papers
Visual Grounding of Whole Radiology Reports for 3D CT Images
Akimichi Ichinose, Taro Hatsutani, Keigo Nakamura +5
Building a large-scale training dataset is an essential problem in the development of medical image recognition systems. Visual grounding techniques, which automatically associate…
Large Batch and Patch Size Training for Medical Image Segmentation
Junya Sato, Shoji Kido
Multi-organ segmentation enables organ evaluation, accounts the relationship between multiple organs, and facilitates accurate diagnosis and treatment decisions. However, only few…
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs
Junya Sato, Yuki Suzuki, Tomohiro Wataya +5
Large numbers of labeled medical images are essential for the accurate detection of anomalies, but manual annotation is labor-intensive and time-consuming. Self-supervised learning…
Weak Supervision in Convolutional Neural Network for Semantic Segmentation of Diffuse Lung Diseases Using Partially Annotated Dataset
Yuki Suzuki, Kazuki Yamagata, Yanagawa Masahiro +2
Computer-aided diagnosis system for diffuse lung diseases (DLDs) is necessary for the objective assessment of the lung diseases. In this paper, we develop semantic segmentation mod…
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-
Aiga Suzuki, Hidenori Sakanashi, Shoji Kido +1
Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks. For image recog…