153 citations · 153 across the 1 of their papers we have counts for
5 papers
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation
Woong Bae, Seungho Lee, Yeha Lee +3
Neural Architecture Search (NAS), a framework which automates the task of designing neural networks, has recently been actively studied in the field of deep learning. However, ther…
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays
Sejin Park, Woochan Hwang, Kyu-Hwan Jung
Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data. In this paper, we explore the self training method, a form of semi-supe…
Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN
Jaemin Son, Woong Bae, Sangkeun Kim +2
Fundoscopic images are often investigated by ophthalmologists to spot abnormal lesions to make diagnoses. Recent successes of convolutional neural networks are confined to diagnose…
False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs
Sejin Park, Woochan Hwang, Kyu Hwan Jung +2
Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive. The performance of supervised algorithms for various tasks on imaging h…
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
Jaemin Son, Sang Jun Park, Kyu-Hwan Jung
Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images. Though many approaches have been proposed, existing method…