The Double Dilemma in Multi-Task Radiology Report Generation: A Gradient Dynamics Analysis and Solution
arXiv:2605.22635v1 Announce Type: new Abstract: While multi-task learning based automatic radiology report generation (RRG) is widely adopted to ensure clinical consistency, most focus on architectural designs yet remain limited to coarse linear scalarization strategies. These strategies cannot effectively balance the hard constraints of discriminative clinical supervision with the smoothness requirements of report generation. To address these problems, we analyze the failure mechanism of linear scalarization from the perspective of gradient dynamics, utilizing the stochastic differential equa
The paper addresses a current limitation in multi-task learning for critical AI applications, aligning with the ongoing drive for more reliable and robust AI systems in healthcare.
Improving the accuracy and consistency of automated radiology report generation directly impacts clinical workflows and patient care, fostering greater trust in AI for medical diagnostics.
This research provides a more sophisticated approach to balancing competing objectives in multi-task AI models, potentially leading to more effective and deployable AI in specialized domains.
- · AI developers in healthcare
- · Radiology departments
- · Patients receiving diagnoses
- · Medical AI software companies
- · Developers relying solely on linear scalarization
- · AI models with suboptimal multi-task balancing
Improved performance and reliability of AI systems in medical imaging analysis.
Accelerated adoption of AI in diagnostics due to enhanced accuracy and clinical consistency.
Reduced burden on human radiologists, allowing them to focus on complex cases and patient interaction.
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Read at arXiv cs.LG