Bibliographic record
Abstract
This thesis explores the use of artificial intelligence (AI) in medical image analysis to address challenges in clinical diagnostics, treatment prediction, and disease prognosis. The work emphasizes the importance of explainability and uncertainty estimation in AI models to ensure transparency and reliability in medical applications. It introduces reliable segmentation and detection tools for various medical conditions, such as head and neck cancer, carotid artery disease, and renal cysts. Additionally, diagnostic and predictive tools were developed for idiopathic pulmonary fibrosis, head and neck cancer survival, and post-hepatectomy liver failure. Novel uncertainty estimation methods were integrated into deep neural networks, improving post-processing, performance, and quality control. The work also explores explainability approaches in both handcrafted radiomics and deep learning, introducing new methods like counterfactual explanations. This thesis proposes a new framework for the methodological evaluation of explanations for AI tools in medical image analysis. It also proposes a new standard for benchmarking radiomics research to improve the clinical translation of radiomics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".