Computer Aided Surgery

Contactless Interface Software

Objective : Self Training & Prediction for contactless gesture recognition in Operating System

               Development of hand gestures recognition using Machine learning for contactless interface system

Gesture : Hover, grab, click, one peak, and two peaks.

SW : Window OS, 64bit, WT PACS, 3DPACS, Common IMAGE Viewer

Sensor Device : Single Sensor or Multiple sensor Kinect, Leap Motion and MYO

Application Device : Amis-50(clinical device PC)

Published paper : Computer Methods and Programs in Biomedicine, Volume 161, July 2018, Pages 39-44

Deep learning based Chest X-ray-CAD system

Chest X-ray CAD (Computer aided diagnosis) system

      For faster reading of radiologists, AI algorithm with deep learning diagnose X-rays in advance with attention

      map. This SW automatically generate radiologic reports including lesion size, location, classification of possible

      diseases, and attention map for indicating the diseases.

Pulmonary lesions : nodule, consolidation(mass), interstitial opacity, pleural effusion, pneumothorax

Algorithm : Convolutional neural net (CNN) with weakly supervision

Overview : lesion size, location, classification, and measurements

ENT surgery video

Monitoring simple mastoidectomy video

To develop monitoring and mentoring SW for physician with training procedures of simple mastoidectomy video with devices detection and anatomic landmark segmentation

Algorithm : CNN, LSTM, YOLO, U-Net, etc

Overview : scene prediction, device detection & landmark segmentation

388-1 Pungnap-2 dong, Songpagu,

Seoul 138-736, Korea

Tel: (052)259-2808/2454

Fax: (052)247-7619

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