Synchronous Image-Label Diffusion Probability Model With Application to Stroke Lesion Segmentation on Non-Contrast CT
Bibliographic record
Abstract
The stroke lesion volume is a key radiologic measurement for assessing the prognosis of acute ischemic stroke (AIS) patients, which is challenging to be automatically measured on noncontrast CT (NCCT) scans. Recent diffusion probabilistic models (DPMs) in the domain of image generation have shown potentials of being used for lesion volume segmentation on medical images. In this article, a novel synchronous image-label diffusion probability model (SDPM) is proposed for stroke lesion segmentation on NCCT using a dual-Markov diffusion process with shared noise. The proposed SDPM is fully based on a generative latent variable model (LVM), offering a probabilistic elaboration from stem to stem. To fit into our segmentation tasks using the strength from generation models, we develop the architecture of the network where an additional net-stream, parallel with a noise prediction stream, is introduced to obtain the initial label estimates with noise for efficiently inferring the final labels. By optimizing the specified variational boundaries, the trained model can infer the final label estimates given the input images at any scale of time in four different label-inference methods, which gives more flexibility to the proposed SDPM. The proposed model was assessed on three stroke lesion datasets including one public and two private datasets. Compared with several U-Net, transformer, and DPM-based segmentation methods, our proposed SDPM model is able to achieve the state-of-the-art accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".