(주) 뷰노

(주) 뷰노

문의사항 남기기
KR EN
Publications
BACK TO LIST

Leveraging the Generalization Ability of Deep Convolutional Neural Networks for Improving Classifiers for Color Fundus Photographs

  • Jan. 2021

Abstract

Deep learning demands a large amount of annotated data, and the annotation task is often crowdsourced for economic efficiency. When the annotation task is delegated to non-experts, the dataset may contain data with inaccurate labels. Noisy labels not only yield classification models with sub-optimal performance, but may also impede their optimization dynamics. In this work, we propose exploiting the pattern recognition capacity of deep convolutional neural networks to filter out supposedly mislabeled cases while training. We suggest a training method that references softmax outputs to judge the correctness of the given labels. This approach achieved outstanding performance compared to the existing methods in various noise settings on a large-scale dataset (Kaggle 2015 Diabetic Retinopathy). Furthermore, we demonstrate a method mining positive cases from a pool of unlabeled images by exploiting the generalization ability. With this method, we won first place on the offsite validation dataset in pathological myopia classification challenge (PALM), achieving the AUROC of 0.9993 in the final submission. Source codes are publicly available.

Keywords: diabetic retinopathy; pathologic myopia; classification; retinal image analysis; fundus image; deep convolutional neural network; Semi-supervised learning

Link

https://www.mdpi.com/2076-3417/11/2/591/htm

Author

Jaemin Son, Jaeyoung Kim, Seo Taek Kong, Kyu-Hwan Jung

Tag in

#medical_image

#VUNO Med® Solution

#VUNO Med®-Fundus AI™

이전글
Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs 2022. 03. 18
다음글
Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement 2022. 01. 19
List

이 웹사이트는 원활한 서비스 제공을 위하여 당사 개인정보 처리방침에 명시된 쿠키를 사용합니다.

만일 당사의 쿠키 사용 정책에 대해 더 알고 싶으신 경우에는 개인정보 처리방침 을 참고하여 주시기 바랍니다.