Get started
Work

Optimizing the Hybrid Loss Function of a Cnn-transformer Model for Brain Mri Segmentation: Search Space, Budgeted Procedure and the Noise Floor

Year: 2026
Type: article
Abstract: Hybrid CNN-Transformer segmentation models are almost always trained with a compound objective that adds cross-entropy, focal and Tversky terms to a Dice loss, and the weights of these terms are commonly described as "optimized". This paper examines what such optimization actually costs and what it can reliably deliver. We formalize the parameter space of a four-term compound objective for the nested BraTS regions, show that an exhaustive grid over the mixing weights, the focal exponent and the Tversky asymmetry contains 7150 candidate configurations and would require approximately 25 000 GPU-hours to evaluate, and propose a three-stage budgeted procedure that reduces this to 31 training runs. Stage one was executed on BraTS 2021 with a 31.8 M-parameter hybrid network under an identical split, seed and schedule: the four-term objective attained the best enhancing-tumor Dice (0.7904) and per-region gains followed the design rationale of each term, but the spread of mean Dice across the four component configurations was only 0.0028. We then show that this spread is the binding constraint on the entire optimization problem. Under a plausible seed-to-seed standard deviation, the expected value of the maximum over N single-run candidates grows as the square root of the logarithm of N, so an exhaustive search over 7150 configurations would report a spurious improvement of roughly 0.013, which is more than four times the largest real effect measured. We derive the maximum number of candidates a single-seed protocol may evaluate before selection bias dominates, and conclude that compound-loss weight optimization on this benchmark is not compute-limited but noise-limited.
DOI: 10.5281/zenodo.23907889
Language: English
Open Access status: green
OpenAlex ID: W7223717599
FWCI:
Cites: 1
Cited by:
Related to: 10