Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gadolinium-enhancing lesions in brain magnetic resonance imaging of multiple sclerosis (MS) patients are of great interest since they are markers of disease activity. Identification of gadolinium-enhancing lesions is particularly challenging because the vast majority of enhancing voxels are associated with normal structures, particularly blood vessels. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic framework for segmentation of gadolinium-enhancing lesions in MS using conditional random fields. Our approach, through the integration of different components, encodes different information such as correspondence between the intensities and tissue labels, patterns in the labels, or patterns in the intensities. The proposed algorithm is evaluated on 80 multimodal clinical datasets acquired from relapsing-remitting MS patients in the context of multicenter clinical trials. The experimental results exhibit a sensitivity of 98% with a low false positive lesion count. The performance of the proposed algorithm is also compared to a logistic regression classifier, a support vector machine and a Markov random field approach. The results demonstrate superior performance of the proposed algorithm at successfully detecting all of the gadolinium-enhancing lesions while maintaining a low false positive lesion count.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it