Journal of Cybersecurity and Information Management
JCIM
2690-6775
2769-7851
10.54216/JCIM
https://www.americaspg.com/journals/show/2967
2019
2019
Panoptic Segmentation with Multi-Modal Dataset Using an Improved Network Model
School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India
Gouranga
Gouranga
School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India
Gouranga
Mandal
For biomedical image analysis, instance segmentation is crucial. It is still difficult because of the intricate backdrop elements, the significant variation in object appearances, the large number of overlapping items, and the hazy object borders. Deep learning-based techniques, which may be separated into proposal-free and proposal-based approaches, have been frequently employed recently to overcome these challenges. The existing approaches experience information loss due to their concentration on either local-level instance features or global-level semantics. To solve this problem, this work proposes an improved dense Net ( ) that mixes instance and semantic data. The suggested promotes the acquisition of semantic contextual information by the instance branch by linking instance prediction and semantic features via a residual attention feature integration strategy. The confidence score of each item is then matched with the accuracy of the prediction using a dense quality sub-branch that is created. A consistency regularisation technique is also proposed for the robust learning of segmentation for instance branches and the semantic segments tasks. By proving its utility, the proposed outperforms prevailing approaches on various biomedical datasets.
2024
2024
101
114
10.54216/JCIM.140207
https://www.americaspg.com/articleinfo/2/show/2967