الصفحة 8
الصفحة 8
Accessing Multilingual Information Repositories ; 6th Workshop of the Cross-Language Evaluation Forum, CLEF 2005, Vienna, Austria, 21-23 September, 2005, Revised Selected Papers
The sixth campaign of the Cross Language Evaluation Forum (CLEF) for European languages was held from January to September 2005. CLEF is by now an established international evaluation initiative and 74 groups from all over the world submitted results for one or more of the different evaluation tracks in 2005, compared with 54 groups in 2004. There were eight distinct evaluation tracks, designed to test the performance of a wide range of systems for multilingual information access. Full details regarding the design of the tracks, the methodologies used for evaluation, and the results obtained by the participants can be found in the different sections of these proceedings.
3D Segmentation for medical images (OsteoVision) = التقطيع ثلاثي الأبعاد للصور الطبية
With the increasing integration of AI across various sectors, artificial intelligence (AI) is already playing a significant role in the healthcare industry, and its use is expected to grow further. AI systems used in image processing and computer vision algorithms have shown a significant ability to perform many operations such as segmentation, classification, and detection. This project presents the application of computer vision algorithms in the field of medical imaging for diagnostic, therapeutic, and interventional purposes. This thesis explores the use of several computer vision algorithms to address different pathologies, specifically brain tumors (glioma) (see Appendix A) and knee osteoarthritis (OA), as well as tracking the progression of knee osteoarthritis using the Kellgren and Lawrence (KL) grading system, a common method for classifying the severity of OA into five grades. To achieve the desired impact, the project employs various techniques, including 3D segmentation for brain tumors, 2D segmentation for knee joints, and multinomial classification for determining the severity of knee OA injuries. The primary aims of the project are to enhance diagnostic accuracy, assist in creating treatment plans, provide an assistive tool for healthcare providers to make more informed decisions, leverage AI's capabilities to detect abnormalities that might escape the human eye, and streamline workflow. To facilitate these goals, the project incorporates a user-friendly UI, a website, and a Flutter-based mobile application, enabling healthcare providers to efficiently integrate these tools into their practice and improve patient care.
