@inproceedings{17df7d35d2314fdb9af2e6f2ce50ae77,
title = "ESFPNet: Efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video",
abstract = "Lung cancer tends to be detected at an advanced stage, resulting in a high patient mortality rate. Thus, recent research has focused on early disease detection. Autofluorescence bronchoscopy (AFB) is an effective noninvasive way of detecting early manifestations of lung cancer. Unfortunately, manual inspection of AFB video is extremely tedious and error-prone, while limited effort has been expended toward potentially more robust automatic AFB lesion analysis. We propose a real-time deep-learning architecture dubbed ESFPNet for accurate segmentation and robust detection of bronchial lesions in an AFB video stream. Our approach gives the best segmentation results (mDice = 0.756, mIoU=0.624) on our AFB dataset among recent architectures. Moreover, our model shows promising potential applicability to other domains, as evidenced by its state-of-the-art (SOTA) performance on the CVC-ClinicDB,ETIS-LaribPolypDB datasets, and superior performance on the Kvasir, CVC-ColonDB datasets.",
keywords = "airway wall analysis, auto uorescence imaging, bronchoscopy, deep learning, efficient stage-wise feature pyramid, lesion segmentation and detection, lung cancer, mix transformer",
author = "Qi Chang and Danish Ahmad and Jennifer Toth and Rebecca Bascom and Higgins, {William E.}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 19-02-2023 Through 22-02-2023",
year = "2023",
doi = "10.1117/12.2647897",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gimi, {Barjor S.} and Andrzej Krol",
booktitle = "Medical Imaging 2023",
address = "United States",
}
