Seokhwan KO

Deep Learning Researcher

-Designed a novel architecture integrating attention mechanisms and clustering methods to enhance interpretability and discriminate abnormalities in thyroid WSIs.

-Designed the 2-step cascaded architecture based on CNN and CRNN(CNN+RNN) to predict TERT promoter mutational status in thyroid cancer.

-Built 3D models to identify the correlation between appendicitis diagnoses from non-contrast CT scan and enhanced CT scan.

Computer Vision Engineer

-Developed a cascaded deep learning architecture, considering diverse window values to enhance the sensitivity of predicting hemorrhages in brain CT images.

-Designed novel deep neural networks by combining the VGG16 architecture with the Unet concept and modified pooling layers based on semantic segmentation method.

-Developed back-end engines for the deep learning medical platform to integrate in front-end UI/UX.

A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation

Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images

Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images

Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models

Kyungpook National University

Tufts University

University of Massachusetts Lowell