35 citations · 49 across the 14 of their papers we have counts for
10 papers · 1 filter
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation
André Ferreira, Tiago Jesus, Behrus Puladi +3
This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due…
Brain Tumour Removing and Missing Modality Generation using 3D WDM
André Ferreira, Gijs Luijten, Behrus Puladi +3
This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support cl…
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
Nikoo Moradi, André Ferreira, Behrus Puladi +5
Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional ima…
Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T MRI
Ana Carolina Alves, André Ferreira, Behrus Puladi +2
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…
A Semi-automatic Cranial Implant Design Tool Based on Rigid ICP Template Alignment and Voxel Space Reconstruction
Michael Lackner, Behrus Puladi, Jens Kleesiek +2
In traumatic medical emergencies, the patients heavily depend on cranioplasty - the craft of neurocranial repair using cranial implants. Despite the improvements made in recent yea…
An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs
Patrick Wienholt, Alexander Hermans, Firas Khader +5
This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regres…
Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review
Ana Carolina Alves, André Ferreira, Gijs Luijten +4
Medical imaging faces challenges such as limited spatial resolution, interference from electronic noise and poor contrast-to-noise ratios. Photon Counting Computed Tomography (PCCT…
Navigating the Peril of Generated Alternative Facts: A ChatGPT-4 Fabricated Omega Variant Case as a Cautionary Tale in Medical Misinformation
Malik Sallam, Jan Egger, Rainer Roehrig +1
In an era where artificial intelligence (AI) intertwines with medical research, the delineation of truth becomes increasingly complex. This study ostensibly examines a purported no…
Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
Johannes Raufeisen, Kunpeng Xie, Fabian Hörst +9
Background: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine…