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Lesion Localization in OCT by Semi-Supervised Object Detection

arXiv 2204.11227 ↗First posted 24 Apr 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated lesion localization in OCT is thus important for detecting retinal diseases at their early stage. To conquer the lack of manual annotation for deep supervised learning, this paper presents a first study on utilizing semi-supervised object detection (SSOD) for lesion localization in OCT images. To that end, we develop a taxonomy to provide a unified and structured viewpoint of the current SSOD methods, and consequently identify key modules in these methods. To evaluate the influence of these modules in the new task, we build OCT-SS, a new dataset consisting of over 1k expert-labeled OCT B-scan images and over 13k unlabeled B-scans. Extensive experiments on OCT-SS identify Unbiased Teacher (UnT) as the best current SSOD method for lesion localization. Moreover, we improve over this strong baseline, with mAP increased from 49.34 to 50.86.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 24 Apr 2022.

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13 of 21 references.

References · 13

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  10. 2020Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorizationcited by 15
  11. 2021Multi-Modal Multi-Instance Learning for Retinal Disease Recognitioncited by 14
  12. 2019Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorizationcited by 11
1 more
  1. 2017Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networkscited by 5

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