Dual Task Learning: A Semi-Supervised Approach to Medical Image Joint Segmentation and Registration
Bibliographic record
Abstract
This work proposes a novel multi-scale attention-enhanced dual-task network, MSA-DTNet, to simultaneously address two critical tasks, segmentation and registration. MSA-DTNet is designed for high-resolution 3D MRI data to incorporate multi-scale convolutions to capture both local and global features and attention mechanisms to enhance the model’s focus on key anatomical structures. By jointly optimizing segmentation and registration tasks, our network improves anatomical consistency and overall performance in medical image processing. The segmentation decoder produces high-quality segmentation maps, while the registration decoder outputs a displacement field for aligning images with a reference. A novel hybrid loss function is also proposed to optimize the model during training. The experiments on the brain MRI dataset demonstrate that MSA-DTNet outperforms existing state-of-the-art networks in terms of dice score (DSC), intersection over union (IoU), precision and recall in segmentation, and DSC and mean squared error (MSE) in registration tasks. Our model also achieves significant performance improvements, even with limited labeled data, by leveraging semi-supervised learning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".