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Found 6 papers, 2 papers with code
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowledge.
An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation
Using an off-the-shelf language model, we distil ground-truth reports into radiological descriptions for each SAE feature, which we then compile into a full report for each image, eliminating the need for fine-tuning large models for this task.
Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data
Existing harmonization techniques, which use statistical models to remove such effects, have been shown to incompletely remove site effects while also failing to preserve biological variability.
Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis
To our knowledge, this is the first use of normative modelling within a diffusion autoencoder, as well as the first application of normative modelling to ALS.
A coupled-mechanisms modelling framework for neurodegeneration
We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects.
Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
Therefore, this work stands as a contribution to the pertinent development of accurate and interpretable deep learning within medical imaging.
