Wykaz obszarów badawczych związanych z tagiem Generative-ai:
| # | Obszar badawczy | Dziedzina naukowa |
|---|---|---|
| 1 |
Development of Explainability Methods for Localizing Vertebral Fractures in Machine Learning-Based Computer-Aided Diagnosis Using Computed Tomography Machine learning-based computer-aided diagnosis in computed tomography (CT) has demonstrated promising performance in automated vertebral fracture classification. However, most supervised learning models are trained using only examination-level or vertebra-level labels and therefore provide little information about the image regions responsible for their predictions. This lack of interpretability limits clinical confidence and hinders the adoption of artificial intelligence in routine radiological practice. Pixel-level annotation of fracture regions could address this issue but is prohibitively time-consuming and expensive because it requires extensive expert involvement. This doctoral research aims to develop explainability methods capable of accurately localizing vertebral fractures using only classification labels during model training. The study will investigate whether explainability maps generated by deep learning classifiers can be transformed into reliable indicators of pathological regions without requiring manually annotated segmentation masks. The research will focus on automated classification of vertebral fractures according to the nine-class AO Spine classification system using three-dimensional CT data. Several deep learning architectures and explainability techniques will be investigated, including gradient-based attribution methods, activation map approaches, and perturbation-based explanations. Novel methodologies will be developed to improve the localization accuracy, anatomical consistency, and clinical interpretability of explainability maps. The proposed methods will be evaluated using expert-annotated clinical CT examinations, with particular emphasis on their ability to identify fracture regions while preserving high classification performance. The expected scientific contribution is the development of explainability methodologies that bridge the gap between image-level classification and lesion localization in medical imaging.
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