White Matter Multiple Sclerosis Lesion Segmentation Under Distributional Shifts / (Record no. 607294)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01758nam a22001697a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Haider, Ali |
| 245 ## - TITLE STATEMENT | |
| Title | White Matter Multiple Sclerosis Lesion Segmentation Under Distributional Shifts / |
| Statement of responsibility, etc. | Ali Haider |
| 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 | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 37p. ; |
| Other physical details | Soft Copy |
| Dimensions | 30cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | In the rapidly evolving era of machine learning and deep learning, new algorithms are<br/>constantly emerging, each built upon existing research and pushing the boundaries in<br/>the field of medical imaging. However, one of the major challenges in the application<br/>of these algorithms is the distributional shifts that occur in real-world datasets. This<br/>research paper utilizes the expanded Shifts 2.0 dataset that was released for The Shift<br/>Challenge 2022. It presents how to enhance the UNET model’s robustness and uncertainty estimations in the segmentation of white matter lesions in Multiple Sclerosis<br/>patients, using only the FLAIR modality. This approach examines the impact of multiple hyperparameters on the results of the Shift 2.0 dataset. The suggested model<br/>yielded R-AUC scores of 1.12 and 1.60 on the Dev-out and Eval-out of the shift dataset,<br/>in contrast to the baseline UNET method which registered scores of 4.66 and 7.40 on<br/>those respective partitions. Moreover, the paper establishes that the performance of an<br/>ensemble of UNET models can be comparable to that of a transformer-based ensemble<br/>of UNETR models, offering promising implications for future research and applications.<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Omer Gilani |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/37077">http://10.250.8.41:8080/xmlui/handle/123456789/37077</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 | 12/11/2023 | 610 | SMME-TH-902 | Thesis |