Differentiable Neural Architecture Search for medical image segmentation: A systematic review and field audit
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View abstract on PubMed
Summary
This summary is machine-generated.Differentiable Neural Architecture Search (DNAS) shows promise for medical image segmentation but faces challenges. A systematic review found limited external validation, code release, and focus on search stability, hindering clinical use.
Area Of Science
- Medical imaging analysis
- Artificial intelligence in healthcare
- Computational pathology
Background
- Medical image segmentation is vital for clinical applications but faces unique challenges like volumetric data and domain shift.
- Traditional Neural Architecture Search (NAS) is computationally expensive for 3D medical image segmentation.
- Differentiable NAS (DNAS) offers a more feasible approach by using gradient-based optimization within a shared supernet.
Purpose Of The Study
- To systematically review the application of Differentiable NAS (DNAS) in medical image segmentation.
- To analyze the methodologies, reporting standards, and limitations of existing DNAS studies in this domain.
- To propose improvements for transparency, comparability, and clinical reproducibility of DNAS for medical segmentation.
Main Methods
- A PRISMA-inspired systematic literature review was conducted, screening multiple databases from 2018 to 2025.
- 33 papers representing 31 unique DNAS methods for medical image segmentation were quantitatively analyzed.
- The review focused on aspects like external validation, code availability, search stability, and multi-objective optimization.
Main Results
- External validation on independent datasets was reported in only ~10% of studies.
- Full code release, including search implementation, was limited to ~26% of the reviewed papers.
- Substantive investigation of search stability and multi-objective optimization (latency, memory) was uncommon (~23% each).
Conclusions
- While DNAS is promising for automated medical image segmentation model design, current practices present significant gaps in validation, reproducibility, and reporting.
- There is a critical need for standardized reporting and methodologies to enhance the clinical deployability of DNAS-derived models.
- A proposed NAS Reporting Card aims to improve transparency and comparability in medical segmentation research.
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