Search Results for author:
Found 12 papers, 5 papers with code
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
We utilized the HNTS-MRG2024 dataset, which consists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn.
Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation
The use of automated tools in clinical practice has increased due to the development of more and more sophisticated and reliable algorithms.
Brain Tumour Removing and Missing Modality Generation using 3D WDM
The lack of information that is usually provided by some of the missing MRI modalities also reduces the reliability of the prediction models trained with all modalities.
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.
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.
