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Found 16 papers, 7 papers with code
LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation
However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR.
Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders.
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields.
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
The model first learns a transformation function from normal control (CN) domain to the patient (PT) domain with latent variables controlling transformation directions.
Subtyping brain diseases from imaging data
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment.
Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics
We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles.
Clinica: an open source software platform for reproducible clinical neuroscience studies
It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines.
Disentangling brain heterogeneity via semi-supervised deep-learning and MRI: dimensional representations of Alzheimer's Disease
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis.
MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD).
Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images
The model was first trained using baseline MRIs from the ADNI2 database and then applied to longitudinal data from ADNI1 and BLSA.
Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective
In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy.
PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks
Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests.
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI.
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data.
