Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.

To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following three categories: normal (NORMAL), non-COVID-19 pneumonia (PNEUMONIA), and COVID-19 pneumonia (COVID). We used two public datasets and private dataset collected from eight hospitals for the development and external validation of our DL model (26,393 CXRs).

Retrospective comparison of clinical outcomes of ultrasound-guided percutaneous cholecystostomy in patients with and without coagulopathy: a single center's experience.

Purpose: To compare the complication rate and clinical outcomes for percutaneous cholecystostomy (PC) in patients with or without coagulopathy.

Materials And Methods: We retrospectively reviewed electronic medical chart of patients who underwent ultrasound-guided PC with a 8.5-F drainage tube for acute cholecystitis between November 2003 and March 2017.

Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells.

Differential bone marrow (BM) cell counting is an important test for the diagnosis of various hematological diseases. However, it is difficult to accurately classify BM cells due to non-uniformity and the lack of reproducibility of differential counting. Therefore, automatic classification systems have been developed in which deep learning is used.