Spyridon Bakas

Orcid: 0000-0001-8734-6482

According to our database1, Spyridon Bakas authored at least 71 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions.
CoRR, 2024

The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).
CoRR, 2024

2023
Federated benchmarking of medical artificial intelligence with MedPerf.
Nat. Mac. Intell., July, 2023

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.
Medical Image Anal., 2023

The Liver Tumor Segmentation Benchmark (LiTS).
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Medical Image Anal., 2023

Framing image registration as a landmark detection problem for better representation of clinical relevance.
CoRR, 2023

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?
CoRR, 2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa).
CoRR, 2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).
CoRR, 2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).
CoRR, 2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting.
CoRR, 2023

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.
CoRR, 2023

Why is the winner the best?
CoRR, 2023

Understanding metric-related pitfalls in image analysis validation.
CoRR, 2023

Detecting Histologic Glioblastoma Regions of Prognostic Relevance.
CoRR, 2023

Why is the Winner the Best?
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Biomedical image analysis competitions: The state of current participation practice.
CoRR, 2022

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment.
CoRR, 2022

MammoDL: Mammographic Breast Density Estimation using Federated Learning.
CoRR, 2022

Metrics reloaded: Pitfalls and recommendations for image analysis validation.
CoRR, 2022

Federated Learning for the Classification of Tumor Infiltrating Lymphocytes.
CoRR, 2022

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation.
CoRR, 2022

Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022

Leveraging 2D Deep Learning ImageNet-trained Models for Native 3D Medical Image Analysis.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022

2021
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
CoRR, 2021

The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients.
CoRR, 2021

The University of California San Francisco Preoperative Diffuse Glioma (UCSF-PDGM) MRI Dataset.
CoRR, 2021

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.
CoRR, 2021

The Medical Segmentation Decathlon.
CoRR, 2021

OpenFL: An open-source framework for Federated Learning.
CoRR, 2021

The Federated Tumor Segmentation (FeTS) Challenge.
CoRR, 2021

Common Limitations of Image Processing Metrics: A Picture Story.
CoRR, 2021

Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient.
CoRR, 2021

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging.
CoRR, 2021

Analyzing magnetic resonance imaging data from glioma patients using deep learning.
Comput. Medical Imaging Graph., 2021

Classification of Infection and Ischemia in Diabetic Foot Ulcers Using VGG Architectures.
Proceedings of the Diabetic Foot Ulcers Grand Challenge - Second Challenge, 2021

Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

2020
ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.
IEEE Trans. Medical Imaging, 2020

The future of digital health with federated learning.
npj Digit. Medicine, 2020

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.
NeuroImage, 2020

Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion.
Biomed. Signal Process. Control., 2020

Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols.
Proceedings of the Medical Imaging 2020: Image-Guided Procedures, 2020

O-Net: An Overall Convolutional Network for Segmentation Tasks.
Proceedings of the Machine Learning in Medical Imaging - 11th International Workshop, 2020

A Deep Network for Joint Registration and Reconstruction of Images with Pathologies.
Proceedings of the Machine Learning in Medical Imaging - 11th International Workshop, 2020

Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019
Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network.
Frontiers Comput. Neurosci., 2019

Multivariate Analysis of Preoperative Magnetic Resonance Imaging Reveals Transcriptomic Classification of de novo Glioblastoma Patients.
Frontiers Comput. Neurosci., 2019

ModelHub.AI: Dissemination Platform for Deep Learning Models.
CoRR, 2019

Accurate and Robust Alignment of Variable-stained Histologic Images Using a General-purpose Greedy Diffeomorphic Registration Tool.
CoRR, 2019

A large annotated medical image dataset for the development and evaluation of segmentation algorithms.
CoRR, 2019

Non-invasive transcriptomic classification of de novo Glioblastoma patients through multivariate quantitative analysis of baseline preoperative multimodal magnetic resonance imaging.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019

Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019


Towards Population-Based Histologic Stain Normalization of Glioblastoma.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019

2018
Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.
NeuroImage, 2018

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.
CoRR, 2018

Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

Patient-Specific Registration of Pre-operative and Post-recurrence Brain Tumor MRI Scans.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018

2017
Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model.
Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2017

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2017

2016
Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2016

2015
GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2015

2014
Computer-aided localisation, segmentation and quantification of focal liver lesions in contrast-enhanced ultrasound.
PhD thesis, 2014

Fast Segmentation of Focal Liver Lesions in Contrast-Enhanced Ultrasound Data.
Proceedings of the Medical Image Understanding and Analysis, 2014

Automatic Identification and Localisation of Potential Malignancies in Contrast-Enhanced Ultrasound Liver Scans Using Spatio-Temporal Features.
Proceedings of the Abdominal Imaging. Computational and Clinical Applications, 2014

Making the Best Use of Fifty (or More) Shades of Gray: Intelligent Contrast Optimisation for Image Segmentation in False-Colour Video.
Proceedings of the 2014 International Conference on Intelligent Environments, Shanghai, China, June 30, 2014

2013
Spot the Best Frame: Towards Intelligent Automated Selection of the Optimal Frame for Initialisation of Focal Liver Lesion Candidates in Contrast-Enhanced Ultrasound Video Sequences.
Proceedings of the 9th International Conference on Intelligent Environments, 2013

2012
Focal Liver Lesion Tracking in CEUS for Characterisation Based on Dynamic Behaviour.
Proceedings of the Advances in Visual Computing - 8th International Symposium, 2012

2011
Localisation and characterisation of focal liver lesions using contrast-enhanced ultrasonographic visual cues.
Proceedings of the Medical Image Understanding and Analysis, 2011


  Loading...