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Found 7 papers, 2 papers with code
TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation
To address this, we propose TiBiX: Leveraging Temporal information for Bidirectional X-ray and Report Generation.
Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical information
The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over the past decade and one common cause of treatment failure is recurrence.
Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration
Deformable image registration, the estimation of the spatial transformation between different images, is an important task in medical imaging.
Ranked #1 on
Image Registration
on Unpaired-lung-CT
Computed Tomography (CT)
Deformable Medical Image Registration
+2
Autopet Challenge 2023: nnUNet-based whole-body 3D PET-CT Tumour Segmentation
Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) combined with Computed Tomography (CT) scans are critical in oncology to the identification of solid tumours and the monitoring of their progression.
Towards Automatic Scoring of Spinal X-ray for Ankylosing Spondylitis
In this study, we address this challenge by prototyping a 2-step auto-grading pipeline, called VertXGradeNet, to automatically predict mSASSS scores for the cervical and lumbar vertebral units (VUs) in X-ray spinal imaging.
An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images
Diabetic retinopathy (DR) is a complication of diabetes, and one of the major causes of vision impairment in the global population.
Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention
The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods of the primary gross target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography images and prediction of progression-free survival in H\&N oropharyngeal cancer. For the segmentation task, we proposed a new network based on an encoder-decoder architecture with full inter- and intra-skip connections to take advantage of low-level and high-level semantics at full scales.
