Deep learning (DL) has achieved remarkable progress in the field of medical
imaging. However, adapting DL models to medical tasks remains a significant
challenge, primarily due to two key factors: (1) architecture selection, as
different tasks necessitate specialized model designs, and (2) weight
initialization, which directly impacts the convergence speed and final
performance of the models. Although transfer learning from ImageNet is a widely
adopted strategy, its effectiveness is constrained by the substantial
differences between natural and medical images. To address these challenges, we
introduce Medical Neural Network Search (MedNNS), the first Neural Network
Search framework for medical imaging applications. MedNNS jointly optimizes
architecture selection and weight initialization by constructing a meta-space
that encodes datasets and models based on how well they perform together. We
build this space using a Supernetwork-based approach, expanding the model zoo
size by 51x times over previous state-of-the-art (SOTA) methods. Moreover, we
introduce rank loss and Fr\’echet Inception Distance (FID) loss into the
construction of the space to capture inter-model and inter-dataset
relationships, thereby achieving more accurate alignment in the meta-space.
Experimental results across multiple datasets demonstrate that MedNNS
significantly outperforms both ImageNet pre-trained DL models and SOTA Neural
Architecture Search (NAS) methods, achieving an average accuracy improvement of
1.7% across datasets while converging substantially faster. The code and the
processed meta-space is available at https://github.com/BioMedIA-MBZUAI/MedNNS.

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