MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fields

Paul Friedrich, Florentin Bieder, Julian McGinnis, Julia Wolleb, Daniel Rueckert, Philippe C. Cattin

Keywords: Generalizable Neural Fields, Implicit Neural Representations, Meta-Learning

TL;DR: MedFuncta efficiently learns continuous neural representations of diverse medical signals using meta-learned shared networks conditioned by compact latent vectors.

Abstract: Research in medical imaging primarily focuses on discrete data representations that poorly scale with grid resolution and fail to capture the often continuous nature of the underlying signal. Neural Fields (NFs) offer a powerful alternative by modeling data as continuous functions. While single-instance NFs have successfully been applied in medical contexts, extending them to large-scale medical datasets remains an open challenge. We therefore introduce **MedFuncta**, a unified framework for large-scale NF training on diverse medical signals. Building on Functa, our approach encodes data into a unified representation, namely a 1D latent vector, that modulates a shared, meta-learned NF, enabling generalization across a dataset. We revisit common design choices, introducing a non-constant frequency parameter $\omega$ in widely used SIREN activations, and establish a connection between this $\omega$-schedule and layer-wise learning rates, relating our findings to recent work in theoretical learning dynamics. We additionally introduce a scalable meta-learning strategy for shared network learning that employs sparse supervision during training, thereby reducing memory consumption and computational overhead while maintaining competitive performance. Finally, we evaluate MedFuncta across a diverse range of medical datasets and show how to solve relevant downstream tasks on our neural data representation. To promote further research in this direction, we release our code, model weights and the first large-scale dataset - **MedNF** - containing > 500 k latent vectors for multi-instance medical NFs. The project page is available at: https://pfriedri.github.io/medfuncta-io.

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Application: Other

Registration Requirement: Yes

Reproducibility: https://github.com/pfriedri/medfuncta/

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

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Submission Number: 8