Publications


NeurIPS 2018 ML4H Workshop | 2018-12-08

Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays


Abstract

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-supervised learning, to address the labeling burden. By integrating reinforcement learning, we were able to expand the application of self training to complex segmentation networks without any further human annotation. The proposed approach, reinforced self training (ReST), fine tunes a semantic segmentation networks by introducing a policy network that learns to generate pseudolabels. We incorporate an expert demonstration network, based on inverse reinforcement learning, to enhance clinical validity and convergence of the policy network. The model was tested on a pulmonary nodule segmentation task in chest X-rays and achieved the performance of a standard U-Net while using only 50% of the labeled data, by exploiting unlabeled data. When the same number of labeled data was used, a moderate to significant cross validation accuracy improvement was achieved depending on the absolute number of labels used.
RSNA 2018 | 2018-11-27

Deep Learning-Based Computer-Aided Detection System for Multiclass Multiple Lesions on Chest Radiographs: Observers’ Performance Study


Abstract

To evaluate the added value of a deep-learning based computer-aided detection (CAD) system for multiclass multiple lesions on radiographs when radiologists read chest radiographs.
RSNA 2018 | 2018-11-27

Deep Learning-Based Automatic Chest PA Screening System for Various Devices and Hospitals


Abstract

To ensure generalization performance in various hospitals, we developed a deep learning based automatic Chest PA screening System which can detect 5 class findings and performs well on various devices. Its performance was evaluated by using FROC and FOM in various devices.
RSNA 2018 | 2018-11-25

CNN-based Image Super-Resolution for CT Slice Thickness Reduction using Paired CT Scans for Improving Robustness of Computer-aided Nodule Detection System


Abstract

To evaluate the effectiveness of a slice thickness reduction technique in computed tomography(CT) scans using convolutional neural network(CNN)-based super-resolution(SR) network for improving the sensitivity of lung nodule detection in thick section CT scans.
PLOS ONE | 2018-10-15

Validation of deep-learning-based triage and acuity score using a large national dataset


Abstract

Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset. We conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables). A total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179). Deep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset.

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