Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques
arXiv:2512.14937v2 Announce Type: replace-cross Abstract: Gliomas are the most common malignant brain tumors in adults and are among the most lethal. Despite aggressive treatment, the median survival rate is less than 15 months. Accurate multiparametric MRI (mpMRI) tumor segmentation is critical for surgical planning, radiotherapy, and disease monitoring. While deep learning models have improved the accuracy of automated segmentation, large-scale pre-trained models generalize poorly and often underperform, producing systematic errors such as false positives, label swaps, and slice discontinuit
The paper addresses the current challenge of generalizing pre-trained deep learning models for medical image segmentation, a critical bottleneck in deploying AI in clinical settings.
Improving the accuracy and reliability of AI models in medical diagnostics directly impacts patient outcomes, operational efficiency in healthcare, and the trustworthiness of AI in critical applications.
This research suggests that existing, potentially underperforming pre-trained medical AI models can be significantly improved with post-processing, potentially extending their useful life and reducing the immediate need for entirely new, highly specialized models.
- · Healthcare providers
- · Medical AI companies
- · Patients with gliomas
- · Deep learning researchers
- · Developers of overly complex, unadaptable AI models
- · Organizations relying solely on out-of-the-box pre-trained models
Increased diagnostic accuracy for adult gliomas using AI, leading to better treatment planning.
Accelerated adoption of AI in clinical radiology as its reliability improves.
Enhanced trust in AI for other critical medical applications, driving further research and integration.
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Read at arXiv cs.AI