A transfer learning coupled framework for distortion classification in laparoscopic videos
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17257-x.pdf
Reference32 articles.
1. Kletz S, Schoeffmann K, Benois-Pineau J, Husslein H (2019) Identifying surgical instruments in laparoscopy using deep learning instance segmentation. In: 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, Dublin, Ireland, pp 1–6. https://doi.org/10.1109/CBMI.2019.8877379
2. Venkatesh V, Sharma N, Srivastava V, Singh M (2020) Unsupervised smoke to desmoked laparoscopic surgery images using contrast driven Cyclic-DesmokeGAN. Comput Biol Med 123:103873
3. Acharya D, Guda RKS, Raovenkatajammalamadaka K (2022) Enhanced efficientnet network for classifying laparoscopy videos using transfer learning technique. In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, Padua, Italy, pp 1–9. https://doi.org/10.1109/IJCNN55064.2022.9891989
4. Wang C, Mohammed AK, Cheikh FA, Beghdadi A, Elle OJ (2019) Multiscale deep desmoking for laparoscopic surgery. InMedical Imaging 2019: Image Processing SPIE 10949:505–513
5. Münzer B, Schoeffmann K, Böszörmenyi L (2018) Content-based processing and analysis of endoscopic images and videos: A survey. Multimed Tools Appl 77:1323–1362
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