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Found 27 papers, 14 papers with code
Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging
Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features.
KindMed: Knowledge-Induced Medicine Prescribing Network for Medication Recommendation
Extensive adoption of electronic health records (EHRs) offers opportunities for its use in various clinical analyses.
A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis
Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately.
A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions.
Deep Geometric Learning with Monotonicity Constraints for Alzheimer's Disease Progression
We verify our proposed method's efficacy by predicting clinical labels and cognitive scores over time in regular and irregular settings.
Domain Generalization for Medical Image Analysis: A Survey
Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances.
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learning
Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD).
SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images.
XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning
By considering this pseudo map as an enriched reference, we employ an estimating network to estimate the AD likelihood map over a 3D sMRI scan.
TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging
Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep learning-based sleep staging.
Deep Efficient Continuous Manifold Learning for Time Series Modeling
Modeling non-Euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields.
Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model
Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model.
Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis
Various data-driven approaches have been introduced to capture subtle and local morphological changes of the brain accompanied by the disease progression.
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis
For building a source model generally applicable to various tasks, we pre-train the model in a self-supervised learning manner for masked encoding vector prediction as a proxy task, using a large-scale normal, healthy brain magnetic resonance imaging (MRI) dataset.
Fine-Grained Attention for Weakly Supervised Object Localization
Although recent advances in deep learning accelerated an improvement in a weakly supervised object localization (WSOL) task, there are still challenges to identify the entire body of an object, rather than only discriminative parts.
Multi-view Integration Learning for Irregularly-sampled Clinical Time Series
In addition, we build an attention-based decoder as a missing value imputer that helps empower the representation learning of the inter-relations among multi-view observations for the prediction task, which operates at the training phase only.
Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier's Decision
Specifically, our proposed BIN consists of two core components: Counterfactual Map Generator and Target Attribution Network.
A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs
In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods.
Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression
While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling (DPM).
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
In this work, we propose a novel variational-recurrent imputation network, which unifies an imputation and a prediction network by taking into account the correlated features, temporal dynamics, as well as the uncertainty.
Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction
However, once the missing values are imputed, most existing methods do not consider the fidelity or confidence of the imputed values in the modeling of downstream tasks.
Multi-Scale Neural network for EEG Representation Learning in BCI
Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal electroencephalogram signal representation learning.
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI
In this paper, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning.
A Plug-in Method for Representation Factorization in Connectionist Models
In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the original trained models.
Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection
Lung cancer is a global and dangerous disease, and its early detection is crucial to reducing the risks of mortality.
Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis
Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging data are related to each other in some ways.
Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis
We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
