Volume 14 , Issue 2 , PP: 101-114, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Koppagiri Jyothsna Devi 1 , Gouranga Mandal 2 *
Doi: https://doi.org/10.54216/JCIM.140207
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.
panoptic segmentation , multi-modal , prediction: semantic features , instance
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