PnP-CoSMo: A Multi-Contrast MRI Reconstruction Framework based on Content/Style Modeling [R]

Reddit r/MachineLearning 07/16/26, 01:10 PM Papers

Summary

PnP-CoSMo is a plug-and-play framework for multi-contrast MRI reconstruction that learns content/style models from image data, enabling reconstruction without raw k-space training data. It is generalizable across contrasts and forward operators.

What is the shared structural essence that underlies a pair of MRI contrast spaces? Explicitly modeling this contrast-invariant latent “content” unlocks a powerful multi-contrast reconstruction algorithm that is competitive with state-of-the-art unrolled networks while: PnP-CoSMo: A plug-and-play framework for multi-contrast MRI reconstruction based on content/style modeling. The first stage learns the content/style model from purely image-domain data. The second stage freezes this model and applies it as a powerful prior in iterative reconstruction. Requiring no raw k-space training data (which is a serious data bottleneck in the ML-based MRI world), Being generalizable across different MR contrasts and forward operators by design, and Offering a built-in explanatory framework. In our paper now published in Medical Image Analysis, we introduce PnP-CoSMo. Read the substack article here (with links to the MedIA paper and code): https://cnmyro.substack.com/p/pnp-cosmo-a-plug-and-play-method

Original Article

Similar Articles

Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation

arXiv cs.AI

This paper proposes a Partial Information Decomposition framework to select the most informative pair of MRI contrasts for brain tumor segmentation using lightweight 3D U-Nets, reducing computational cost while maintaining performance.

Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles

arXiv cs.LG

This paper extends the MoCoP method by using deep-learning-based morphology profiles to improve molecular embeddings for QSAR predictions and toxicity assessment in drug discovery.

GoCoMA: Hyperbolic Multimodal Representation Fusion for Large Language Model-Generated Code Attribution

arXiv cs.CL

GoCoMA is a multimodal framework using hyperbolic Poincaré ball embeddings to fuse code stylometry and binary artifact images for attributing LLM-generated code, outperforming unimodal and Euclidean baselines on two benchmarks.

Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views

arXiv cs.LG

This paper introduces CoCoS, a contrastive pretraining framework that learns whole-cell representations from complementary transcriptomic views, addressing limitations of masked gene reconstruction in single-cell foundation models. Experiments on cell-type annotation and gene regulatory network inference show competitive transfer performance.

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

Hugging Face Daily Papers

MedPMC is an automated framework that transforms medical literature into high-fidelity multimodal data for foundation models, achieving significant improvements across multiple benchmarks and clinical settings.