Search Results for author:
Found 15 papers, 5 papers with code
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
This work proposes a novel method to estimate segmentation uncertainty by leveraging global information from the segmentation masks.
Attention-based Dynamic Subspace Learners for Medical Image Analysis
This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features.
Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation
The learnt labeling representation is used to map the prediction of the segmentation into a set of plausible masks.
Medial Spectral Coordinates for 3D Shape Analysis
In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds.
Realistic Image Normalization for Multi-Domain Segmentation
This paper proposes to revisit the conventional image normalization approach by instead learning a common normalizing function across multiple datasets.
Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020.
Source-Relaxed Domain Adaptation for Image Segmentation
Our formulation is based on minimizing a label-free entropy loss defined over target-domain data, which we further guide with a domain invariant prior on the segmentation regions.
Manifold-driven Attention Maps for Weakly Supervised Segmentation
Segmentation using deep learning has shown promising directions in medical imaging as it aids in the analysis and diagnosis of diseases.
Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis
To ensure that the generated diffusion tensors lie on the SPD(3) manifold, we exploit the theoretical properties of the exponential and logarithm maps of the Log-Euclidean metric.
Image-to-Image Translation
Vocal Bursts Intensity Prediction
Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation
The varying cortical geometry of the brain creates numerous challenges for its analysis.
Adversarial normalization for multi domain image segmentation
To solve this problem, we propose an adversarial normalization approach for image segmentation which learns common normalizing functions across multiple datasets while retaining image realism.
Spectral Graph Transformer Networks for Brain Surface Parcellation
Our alignment and graph processing method provides a fast analysis of brain surfaces.
Learnable Pooling in Graph Convolution Networks for Brain Surface Analysis
This paper proposes a new learnable graph pooling method for processing multiple surface-valued data to output subject-based information.
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.
Ranked #1 on
Medical Image Segmentation
on iSEG 2017 Challenge
Graph Convolutions on Spectral Embeddings: Learning of Cortical Surface Data
This paper presents a novel approach for learning and exploiting surface data directly across surface domains.
