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Found 12 papers, 4 papers with code
Joint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD).
Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs
AD classification and focus on differential diagnosis of dementia, where fusing multi-modal image information conforms with a clinical need.
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
To this end, we introduce CASHformer, a transformer-based framework to model longitudinal shape trajectories in AD.
TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes
To the best of our knowledge, this is the first work that combines transformer and mesh networks.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Disease
Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations.
Recalibration of Neural Networks for Point Cloud Analysis
First, we demonstrate the benefit and versatility of our proposed modules by incorporating them into three state-of-the-art networks for 3D point cloud analysis: PointNet++, DGCNN, and RSCNN.
Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks
The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimers disease, to the point that they can be used to train a discriminative model for disease classification.
Recalibrating 3D ConvNets with Project & Excite
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers.
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
