AI-based automated Meibomian gland segmentation, classification and reflection correction in infrared Meibography

no code implementations • 31 May 2022 • Ripon Kumar Saha, A. M. Mahmud Chowdhury, Kyung-Sun Na, Gyu Deok Hwang, Youngsub Eom, Jaeyoung Kim, Hae-Gon Jeon, Ho Sik Hwang, Euiheon Chung

Purpose: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.

Generative Adversarial Network Segmentation

TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes

no code implementations • 16 May 2019 • Tae Joon Jun, Youngsub Eom, Dohyeun Kim, Cherry Kim, Ji-Hye Park, Hoang Minh Nguyen, Daeyoung Kim

Although there is a Ranking-CNN that takes into account the ordinal classes, it cannot reflect the inter-class relationship to the final prediction.

General Classification Image Classification +3

Tournament Based Ranking CNN for the Cataract grading

no code implementations • 7 Jul 2018 • Dohyeun Kim, Tae Joon Jun, Daeyoung Kim, Youngsub Eom

Because the case with a serious degree is not quite usual, there are imbalance in number of dataset between severe case and normal cases of diseases.

2sRanking-CNN: A 2-stage ranking-CNN for diagnosis of glaucoma from fundus images using CAM-extracted ROI as an intermediate input

no code implementations • 15 May 2018 • Tae Joon Jun, Dohyeun Kim, Hoang Minh Nguyen, Daeyoung Kim, Youngsub Eom

In this paper, we propose a 2-stage ranking-CNN that classifies fundus images as normal, suspicious, and glaucoma.