Search Results for author:
Found 25 papers, 4 papers with code
Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction
We use an attention-based approach that weighs the importance of different slide regions in producing the final classification results.
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
An image-to-image translation strategy between imaging modalities is used to produce annotated pseudo-target volumes and improve generalization to the unannotated target modality.
End-to-end Deformable Attention Graph Neural Network for Single-view Liver Mesh Reconstruction
Intensity modulated radiotherapy (IMRT) is one of the most common modalities for treating cancer patients.
Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction
Accuracies of 85. 8% and 75. 3% was found for radionecrosis and hospitalization, respectively, with similar performance as early as after the first week of treatment.
Prediction of a T-cell/MHC-I-based immune profile for colorectal liver metastases from CT images using ensemble learning
To this end, we propose an ensemble network combining multiple Attentive Interpretable Tabular learning (TabNet) models, trained using CT-derived radiomic features.
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision
Then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality.
Label noise in segmentation networks : mitigation must deal with bias
In this work, we explore biased and unbiased errors artificially introduced to brain tumour annotations on MRI data.
3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications
Moreover, it is found that the speckle reduction using our deep learning model contributes to improving the 3D registration performance.
A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction
In medical imaging, radiological scans of different modalities serve to enhance different sets of features for clinical diagnosis and treatment planning.
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction
Medical image classification performance worsens in multi-domain datasets, caused by radiological image differences across institutions, scanner manufacturer, model and operator.
Towards annotation-efficient segmentation via image-to-image translation
Specifically, we propose a semi-supervised framework that employs unpaired image-to-image translation between two domains, presence vs. absence of cancer, as the unsupervised objective.
End-to-End Discriminative Deep Network for Liver Lesion Classification
In the present work we introduce an end-to-end deep learning approach to assist in the discrimination between liver metastases from colorectal cancer and benign cysts in abdominal CT images of the liver.
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.
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide.
Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US
The objective of this study is to develop an approach quantifying the ratio of lateral ventricular dilatation with respect to total brain volume using 3D US, which can assess the severity of macrocephaly.
Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis
A discriminant manifold is first constructed to maximize the separation between responsive and non-responsive groups of patients treated with AVBGM for scoliosis.
On orthogonality and learning RNNs with long term dependencies
We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.
Liver lesion segmentation informed by joint liver segmentation
We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes.
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.
On orthogonality and learning recurrent networks with long term dependencies
We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.
3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds
Rate of progression is modulated from the spine flexibility and curve magnitude of the 3D spine deformation.
The Importance of Skip Connections in Biomedical Image Segmentation
In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation.
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation.
Sub-cortical brain structure segmentation using F-CNN's
In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data.
Metastatic liver tumour segmentation from discriminant Grassmannian manifolds
The early detection, diagnosis and monitoring of liver cancer progression can be achieved with the precise delineation of metastatic tumours.
