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Found 12 papers, 1 papers with code
3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia
In this paper, we present a novel 3D transformer-based architecture using a deformable patch location module to improve the differential diagnosis of Alzheimer's disease and Frontotemporal dementia.
Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models
Colorization of line art drawings is an important task in illustration and animation workflows.
Brain Structure Ages -- A new biomarker for multi-disease classification
Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure.
Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis
In the first stage, we propose a deep grading model to extract meaningful features.
Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia
In this paper, we propose a deep learning based approach for both problems of disease detection and differential diagnosis.
Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia
However, differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms.
Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence
Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques.
Analysis of Different Losses for Deep Learning Image Colorization
While learning to automatically colorize an image, one can define well-suited objective functions related to the desired color output.
Influence of Color Spaces for Deep Learning Image Colorization
In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question: "Is it crucial to correctly choose the right color space in deep-learning based colorization?".
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors
A fast multi-scale non-local matching framework is also introduced for the search of similar descriptors at different resolution levels in an image dataset.
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Finally, we showed the interest of using semi-supervised learning to improve the performance of our method.
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.
