YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection

• URL: http://arxiv.org/abs/2306.03686v2
• Date: Sun, 30 Jul 2023 14:14:38 GMT
• Title: YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection
• Authors: Yuncheng Jiang, Zixun Zhang, Ruimao Zhang, Guanbin Li, Shuguang Cui, Zhen Li
• Abstract summary: textbfYONA (textbfYou textbfOnly textbfNeed one textbfAdjacent Reference-frame) is an efficient end-to-end training framework for video polyp detection. Our proposed YONA outperforms previous state-of-the-art competitors by a large margin in both accuracy and speed.
• Score: 80.68520401539979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate polyp detection is essential for assisting clinical rectal cancer diagnoses. Colonoscopy videos contain richer information than still images, making them a valuable resource for deep learning methods. Great efforts have been made to conduct video polyp detection through multi-frame temporal/spatial aggregation. However, unlike common fixed-camera video, the camera-moving scene in colonoscopy videos can cause rapid video jitters, leading to unstable training for existing video detection models. Additionally, the concealed nature of some polyps and the complex background environment further hinder the performance of existing video detectors. In this paper, we propose the \textbf{YONA} (\textbf{Y}ou \textbf{O}nly \textbf{N}eed one \textbf{A}djacent Reference-frame) method, an efficient end-to-end training framework for video polyp detection. YONA fully exploits the information of one previous adjacent frame and conducts polyp detection on the current frame without multi-frame collaborations. Specifically, for the foreground, YONA adaptively aligns the current frame's channel activation patterns with its adjacent reference frames according to their foreground similarity. For the background, YONA conducts background dynamic alignment guided by inter-frame difference to eliminate the invalid features produced by drastic spatial jitters. Moreover, YONA applies cross-frame contrastive learning during training, leveraging the ground truth bounding box to improve the model's perception of polyp and background. Quantitative and qualitative experiments on three public challenging benchmarks demonstrate that our proposed YONA outperforms previous state-of-the-art competitors by a large margin in both accuracy and speed.

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