A Comparative Study of Noisy Label Detection Techniques in a Thai Hospital's Chest X-Ray Database

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Tatitaisakul S., Wilainuch S., Chamveha I., Chaisangmongkon W.

ผู้เผยแพร่: Institute of Electrical and Electronics Engineers Inc.

ปีที่เผยแพร่ (ค.ศ.): 2024

หน้าแรก: 232

หน้าสุดท้าย: 237

จำนวนหน้า: 6

ISBN: 9798350344349

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189939072&doi=10.1109%2fICAIIC60209.2024.10463316&partnerID=40&md5=c341303e9065856ade48c73d9839b1e3

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

This paper addresses the problem of noisy labels in chest X-ray datasets, which significantly impact the training of deep neural network models. Noisy labels often occur due to errors in reports from experts or the use of algorithms to extract labels from medical reports written in natural language. To tackle this issue, we compared the effectiveness of O2U-net, a state-of-The-Art noisy label detection method, and NVUM, a noise-resistant model training technique in identifying noisy samples. We contrasted these methods with a heuristic approach which uses a simple classification model to flag samples with large differences between predicted and actual labels as noisy. Our findings indicated that NVUM outperformed the other methods in identifying noisy labels, providing a promising solution to the challenge of noisy labels in medical image analysis. © 2024 IEEE.

คำสำคัญ

Chest X-ray image, noise robust training, Noisy label detection