Search Results for author:
Found 16 papers, 6 papers with code
Subthalamic Nucleus segmentation in high-field Magnetic Resonance data. Is space normalization by template co-registration necessary?
Deep Brain Stimulation (DBS) is one of the most successful methods to diminish late-stage Parkinson's Disease (PD) symptoms.
Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T MRI
The development of magnetic resonance imaging (MRI) for medical imaging has provided a leap forward in diagnosis, providing a safe, non-invasive alternative to techniques involving ionising radiation exposure for diagnostic purposes.
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
Deep Learning is the state-of-the-art technology for segmenting brain tumours.
Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review
This review delves into the recent developments and applications of PCCT in pre-clinical research, emphasizing its potential to overcome traditional imaging limitations.
Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!
In this study, the generation of radiology impressions in different languages was automated by fine-tuning a model, publicly available, based on a multilingual text-to-text Transformer to summarize findings available in English, Portuguese, and German radiology reports.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Treatment And Follow-Up Guidelines For Multiple Brain Metastases: A Systematic Review
Brain metastases are a complication of primary cancer, representing the most common type of brain tumor in adults.
Open-Source Skull Reconstruction with MONAI
The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework.
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy.
FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy
In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs.
Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN
The generated scans were also used to evaluate their impact on the performance of an existing deep learning model developed for segmenting the rat brain into white matter, grey matter and cerebrospinal fluid.
Combining unsupervised and supervised learning for predicting the final stroke lesion
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention.
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks - the SegSE block.
Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
In this way, we overcome the need for expert annotations of regions of interest.
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life.
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI
However, this is not optimal in FCN due to the spatial correspondence between units and voxels.
