Search Results for author:
Found 6 papers, 2 papers with code
Visual Grounding of Whole Radiology Reports for 3D CT Images
Our framework combines two components of 1) anatomical segmentation of images, and 2) report structuring.
Large Batch and Patch Size Training for Medical Image Segmentation
Multi-organ segmentation enables organ evaluation, accounts the relationship between multiple organs, and facilitates accurate diagnosis and treatment decisions.
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs
Large numbers of labeled medical images are essential for the accurate detection of anomalies, but manual annotation is labor-intensive and time-consuming.
Weak Supervision in Convolutional Neural Network for Semantic Segmentation of Diffuse Lung Diseases Using Partially Annotated Dataset
In this paper, we propose a new weak supervision technique that effectively utilizes partially annotated dataset.
Automatic Classification of Large-Scale Respiratory Sound Dataset Based on Convolutional Neural Network
However, there is no quantitative evaluation method for the diagnosis of respiratory sounds until now.
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-
Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks.
