Semi-Supervised Adversarial Transfer Learning for Automated Skin Lesion Segmentation
- Resource Type
- Conference
- Authors
- Bishnoi, Ashish; Kannagi, A; Acharjya, Kalyan
- Source
- 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Optimization Computing and Wireless Communication (ICOCWC), 2024 International Conference on. :1-6 Jan, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Engineering Profession
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Signal Processing and Analysis
Wireless communication
Image segmentation
Transfer learning
Magnetic resonance
Switches
Skin
Robustness
Adversarial
Transfer
proposed
generative
imaging
- Language
Semi-supervised adversarial transfer gaining knowledge of (SATL) has been proposed as a powerful method for automatic pores and skin lesion segmentation. This approach aims to transfer knowledge from a categorized supply area to an unlabeled target domain to enhance the segmentation accuracy. The approach uses a generative opposed community (GAN) to study a function area that's then used to switch the segmentation knowledge from the source to the target domain. Experiments have proven that SATL can enhance segmentation accuracy in the target domain by using as few as 2000 supply domain annotations. Usual, SATL provides a powerful method for automatic pores and skin lesion segmentation in domain names with limited amounts of labeled information and will probably revolutionize medical imaging diagnostics.