Novel Hybrid Neural Network Architecture For Multi-modal Brain Tumor mpMRI Segmentation / (Record no. 613225)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02334nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Faizan, Muhammad |
| 245 ## - TITLE STATEMENT | |
| Title | Novel Hybrid Neural Network Architecture For Multi-modal Brain Tumor mpMRI Segmentation / |
| Statement of responsibility, etc. | Muhammad Faizan |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad: |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 73p. |
| Other physical details | Soft Copy, |
| Dimensions | 30cm. |
| 500 ## - GENERAL NOTE | |
| General note | Medical image segmentation is a critical step in clinical decision-making, enabling<br/>precise localization of anatomical structures and lesions. While Convolutional Neural Networks, particularly U-shaped architectures like U-Net, have been popular in<br/>this domain, their limited receptive fields hinder the accurate delineation of anomalies with irregular shapes and sizes. Hybrid approaches integrating convolution and<br/>vision transformers Vision Transformers (ViTs) have demonstrated improved performance due to their ability to capture dependencies over an extended length. However, ViTs are computationally expensive, particularly for volumetric image segmentation, such as MRI, making them challenging to deploy on hardware with limited<br/>resources. To address these challenges, recent studies have revisited convolutional<br/>architectures, leveraging large kernel (LK) depth-wise convolution to emulate the hierarchical transformer’s behavior. Building on this direction, we propose 3D SegUXNet, a novel U-shaped encoder-decoder architecture for volumetric biomedical image<br/>segmentation. Our model introduces the SegUX block, which combines large kernel<br/>depth-wise and point-wise convolutions to enhance the receptive field while maintaining computational efficiency. The addition of a residual block further refines features,<br/>improving model robustness and generalization. Empirical results demonstrate that<br/>3D SegUX-Net consistently outperforms state-of-the-art CNN and transformer methods on multiple benchmarks, including BraTS 2019, BraTS 2020, BraTS 2023, and<br/>organ segmentation of BTCV dataset. The proposed architecture establishes new<br/>SOTA performance in volumetric medical semantic segmentation, combining simplicity, efficiency, and scalability. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Sara Ali |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/50343">http://10.250.8.41:8080/xmlui/handle/123456789/50343</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 03/06/2025 | 629.8 | SMME-TH-1121 | Thesis |