Spyridon Bakas
A5043497448- Radiomics and Machine Learning in Medical Imaging
- Glioma Diagnosis and Treatment
- AI in cancer detection
- Brain Tumor Detection and Classification
- Artificial Intelligence in Healthcare and Education
- MRI in cancer diagnosis
- Medical Imaging and Analysis
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Medical Imaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Privacy-Preserving Technologies in Data
- Epigenetics and DNA Methylation
- Cancer Genomics and Diagnostics
- Advanced X-ray and CT Imaging
- Explainable Artificial Intelligence (XAI)
- Traumatic Brain Injury and Neurovascular Disturbances
- Ferroptosis and cancer prognosis
- Hepatocellular Carcinoma Treatment and Prognosis
- Meningioma and schwannoma management
- Cell Image Analysis Techniques
- COVID-19 diagnosis using AI
- Advanced Fluorescence Microscopy Techniques
- Ultrasound and Hyperthermia Applications
- Digital Radiography and Breast Imaging
Indiana University – Purdue University Indianapolis
2023-2025
Indiana University School of Medicine
2023-2025
Neurological Surgery
2024-2025
University of Pennsylvania
2015-2024
California University of Pennsylvania
2019-2024
University Hospitals of Cleveland
2024
University Health System
2024
University School
2024
Case Western Reserve University
2024
Hospital of the University of Pennsylvania
2024
The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of biomarkers from acquired imaging for purpose high-throughput quantitative analysis (radiomics). Lack reproducibility and validation studies considered to be a major challenge field. Part this lies in scantiness consensus-based guidelines definitions process translating into biomarkers. IBSI therefore seeks provide nomenclature definitions,...
Abstract Gliomas belong to a group of central nervous system tumors, and consist various sub-regions. Gold standard labeling these sub-regions in radiographic imaging is essential for both clinical computational studies, including radiomic radiogenomic analyses. Towards this end, we release segmentation labels features all pre-operative multimodal magnetic resonance (MRI) ( n =243) the multi-institutional glioma collections The Cancer Genome Atlas (TCGA), publicly available Imaging Archive...
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as sub-regions depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting biological properties....
Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large diverse datasets, required for training, is a significant challenge medicine can rarely be found individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy ownership challenges. Federated novel paradigm data-private multi-institutional collaborations, where model-learning...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far most widely investigated medical processing task, but various segmentation typically been organized in isolation, such that algorithm development was driven by need to tackle single clinical problem. We hypothesized method capable performing well on multiple tasks will generalize previously unseen task and potentially outperform...
Semantic segmentation of medical images aims to associate a pixel with label in image without human initialization. The success semantic algorithms is contingent on the availability high-quality imaging data corresponding labels provided by experts. We sought create large collection annotated datasets various clinically relevant anatomies available under open source license facilitate development algorithms. Such resource would allow: 1) objective assessment general-purpose methods through...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine practice. The workflow of complex due several methodological steps and nuances, which often leads inadequate reporting evaluation, poor reproducibility. Available guidelines checklists artificial intelligence predictive modeling include relevant good practices, but they are not tailored radiomic research. There a clear...
The growth of multiparametric imaging protocols has paved the way for quantitative phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics spatiotemporal heterogeneity, can guide personalized planning. This underlined need efficient analytics to derive high-dimensional signatures diagnostic predictive value in this emerging era integrated precision diagnostics. paper presents phenomics toolkit (CaPTk), a new dynamically growing...
Abstract Purpose To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research of radiomics studies. Methods We conducted an online modified Delphi study with group international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members identify the items be voted; Stage#3, four rounds exercise by panelists determine eligible for METRICS their weights. The...
Abstract Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack consistent acquisition protocol, c) quality, d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute “University Pennsylvania Imaging, Genomics, Radiomics”...
Abstract Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing the evidence-based practice of medicine, personalizing patient treatment, reducing costs, improving both provider experience. Unlocking this requires systematic, quantitative evaluation performance medical AI models on large-scale, heterogeneous data capturing diverse populations. Here, meet need, we introduce MedPerf, an open platform for benchmarking in domain....
Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 with 18 different stains, resulting 481 pairs be registered. accuracy evaluated using manually placed landmarks. In total, 256 teams registered for challenge, 10 submitted results, 6 participated workshop. Here, we present...
Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns machine learning may lead non-invasively determining...