Image Segmentation

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13 papers in the last four weeks, up 44% on the four weeks before. 0.1% of all new papers.

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SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
Date pendingShengbo Tan, Rundong Xue, Shipeng Luo +7Image Segmentation Medical Image Segmentation
College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu 610225, China · Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China · College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China +4
Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
Date pendingBehraj Khan, Tahir Qasim Syed, Syed Ahmad Chan BukhariImage Segmentation Promptable Image Segmentation
St. John’s University New York, USA · Institute of Business Administration Karachi Pakistan

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SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Shengbo Tan, Rundong Xue, Shipeng Luo, Zeyu Zhang+6

College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu 610225, China · Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China · College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China+4

Image Segmentation Medical Image Segmentation

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Hepatic vessels in computed tomography scans often suffer from image fragmentation and noise interference, making it difficult to maintain vessel integrity and posing significant challenges for vessel segmentation. To address this issue, we propose an innovative model: SegKAN. First, we improve the conventional embedding module by adopting a novel convolutional network structure for image embedding, which smooths out image noise and prevents issues such as gradient explosion in subsequent stages. Next, we transform the spatial relationships between Patch blocks into temporal relationships to solve the problem of capturing positional relationships between Patch blocks in traditional Vision Transformer models. We conducted experiments on a Hepatic vessel dataset, and compared to the existing state-of-the-art model, the Dice score improved by 1.78%. These results demonstrate that the proposed new structure effectively enhances the segmentation performance of high-resolution extended objects. Code will be available at https://github.com/goblin327/SegKAN

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Latest papers 302

SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift