most citedReinforcing Medical Image Classifier to Improve Generalization on Small Datasets

2 citations · 2 across the 1 of their papers we have counts for

collaborators

7 papers

eess.IV2019

The CNN-based Coronary Occlusion Site Localization with Effective Preprocessing Method

YeongHyeon Park, Il Dong Yun, Si-Hyuck Kang

The Coronary Artery Occlusion (CAO) acutely comes to human, and it highly threats the human's life. When CAO detected, Percutaneous Coronary Intervention (PCI) should be conducted…

cs.LG20192 cited

Reinforcing Medical Image Classifier to Improve Generalization on Small Datasets

Walid Abdullah Al, Il Dong Yun

With the advents of deep learning, improved image classification with complex discriminative models has been made possible. However, such deep models with increased complexity requ…

eess.IV2019

Reinforcement Learning-based Automatic Diagnosis of Acute Appendicitis in Abdominal CT

Walid Abdullah Al, Il Dong Yun, Kyong Joon Lee

Acute appendicitis characterized by a painful inflammation of the vermiform appendix is one of the most common surgical emergencies. Localizing the appendix is challenging due to i…

cs.CV2019

Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization

Walid Abdullah Al, Il Dong Yun, Eun Ju Chun

Left atrial appendage (LAA) closure (LAAC) is a minimally invasive implant-based method to prevent cardiovascular stroke in patients with non-valvular atrial fibrillation. Assessin…

cs.CV2018

Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks

Dong Yul Oh, Il Dong Yun

Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic task…

cs.LG2018

Comparison of RNN Encoder-Decoder Models for Anomaly Detection

YeongHyeon Park, Il Dong Yun

In this paper, we compare different types of Recurrent Neural Network (RNN) Encoder-Decoders in anomaly detection viewpoint. We focused on finding the model that can learn the same…