Stroke lesion localization in 3D MRI datasets with deep reinforcement learning
Bibliographic record
Abstract
The efficacy of stroke treatments is highly time-sensitive, and any computer-aided diagnosis support method that can accelerate diagnosis and treatment initiation may improve patient outcomes. Within this context, lesion identification in MRI datasets can be time consuming and challenging, even for trained clinicians. Automatic lesion localization can expedite diagnosis by flagging datasets and corresponding regions of interest for further assessment. In this work, we propose a deep reinforcement learning agent to localize acute ischemic stroke lesions in MRI images. Therefore, we adapt novel techniques from the computer vision domain to medical image analysis, allowing the agent to sequentially localize multiple lesions in a single dataset. The proposed method was developed and evaluated using a database consisting of fluid attenuated inversion recovery (FLAIR) MRI datasets from 466 ischemic stroke patients acquired at multiple centers. 372 patients were used for training while 94 patients (20% of available data) were employed for testing. Furthermore, the model was tested using 58 datasets from an out-of-distribution test set to investigate the generalization error in more detail. The model achieved a Dice score of 0.45 on the hold-out test set and 0.43 on images from the out-of-distribution test set. In conclusion, we apply deep reinforcement learning to the clinically well-motivated task of localizing multiple ischemic stroke lesions in MRI images, and achieve promising results validated on a large and heterogeneous collection of datasets.
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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 | 0.002 | 0.006 |
| 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".