Investigating ROI-independent Segmentation and Classification of Glioma in MR Images and of Liver Fibrosis Detection in CT Images
Bibliographic record
Abstract
Segmentation and classification of anomalies is a critical problem in medical imaging. Machine learning has demonstrated potential in automating this problem but generally relies on manually annotated segmentations or regions of interest to train the machine learning models. Acquiring the manual annotations demands extensive time and resources from radiologists, and hinders the development and deployment of machine learning solutions for medical imaging problems without fully annotated datasets. This thesis presents a novel approach to brain tumor segmentation in magnetic resonance images that uses generative adversarial networks to remove the need for manual annotations. These segmentations can be successfully applied to downstream clinical tasks such as genetic marker prediction and pathology prediction. This thesis also presents the optimal approach to liver fibrosis detection in computed tomography images using Radiomics and insights into how to develop liver fibrosis detection solutions without the need for manually annotated regions of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".