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Found 7 papers, 3 papers with code
Poisson flow consistency models for low-dose CT image denoising
In this paper, we introduce a novel image denoising technique which combines the flexibility afforded in Poisson flow generative models (PFGM)++ with the, high quality, single step sampling of consistency models.
Scout-Net: Prospective Personalized Estimation of CT Organ Doses from Scout Views
To optimize CT acquisitions before scanning, rapid prediction of patient-specific organ dose is needed prospectively, using available scout images.
Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data
MEDL separately quantifies cluster-invariant fixed effects (FE) and cluster-specific random effects (RE) through the introduction of: 1) a cluster adversary which encourages the learning of cluster-invariant FE, 2) a Bayesian neural network which quantifies the RE, and a mixing function combining the FE an RE into a mixed-effect prediction.
Semi-Supervised Relational Contrastive Learning
Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts.
Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data
Semi-supervised learning from limited quantities of labeled data has shown promise as an alternative.
Window-Level is a Strong Denoising Surrogate
However, those approaches require access to large training sets, specifically the full dose CT images for reference, which can often be difficult to obtain.
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
Our extensive experimentation with varied quantities of labeled data in the training sets justify the effectiveness of our multitasking model for the classification of pneumonia and segmentation of lungs from chest X-ray images.
