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Spår: MIE: Sensors, signals and Imaging Informatics
Alla programpunkterAI-Based Gut-Brain Axis Digital Twins
Stephane Meystre
Onsdag 24 maj 2023 15:05 - 15:10 G4
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Inspiration, Fördjupning, Chef/Beslutsfattare, Forskare (även studerande), Studerande, Omsorgspersonal, Vårdpersonal, Patientorganisationer/Brukarorganisationer, Personcentrering, Innovativ/forskning
More than 40% of the adult population suffers from functional gastrointestinal disorders, now considered disorders of the “gut-brain axis” (GBA) interactions, a very complex bidirectional neural, endocrine, immune, and humoral communication system modulated by the microbiota. To help discover, understand, and manage GBA disorders, the OnePlanet research center is developing digital twins focused on the GBA, combining novel sensors with artificial intelligence algorithms providing descriptive, diagnostic, predictive or prescriptive feed-back.
A remote monitoring platform for the management of lower limb vascular diseases
Alberto Freitas, Julio Souza
Onsdag 24 maj 2023 15:00 - 15:05 G4
MIE: Sensors, signals and Imaging Informatics, English, Förinspelat + På plats, Presentation, Avancerad
AUCMEDI: a framework for Automated Classification of Medical Images
Dominik Müller, Florian Auer
Måndag 22 maj 2023 10:00 - 14:00 R15
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Annat, Avancerad
Separate registration required: https://www.mie2023.org/tutorials, The open-source Python framework AUCMEDI offers a solution to the describedchallenges. The software package not only offers a library as a 'high-level' API for thestandardized construction of modern medical image classification pipelines, but alsoreproducible installation and direct application via Dockerization and automatichyperparameter detection. With AUCMEDI, researchers are able to set up a completeas well as easy-to-integrate medical image classification pipeline with just a few linesof code. AUCMEDI is available as a Python package via PyPI ('pip install aucmedi')and as a repository via GitHub with detailed documentation, examples, and bindings tomodern DevOps (CI/CD) techniques: https://frankkramer-lab.github.io/aucmedi/.
Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG
Theresa Bender
Onsdag 24 maj 2023 13:45 - 14:00 G4
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
We benchmark the influence of four types of noise annotated in the public dataset PTB-XL on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead ECGs. For this we use the noise metadata provided by human experts as well as a quantitative signal-to-noise ratio for assigning a signal quality to each ECG and analyze the respective DL accuracy. We conclude that the issue of processing noisy electrocardiography data can be addressed successfully by Deep Learning methods.
Can Synthetic Images Improve CNN Performance in Wound Image Classification?
LEILA MALIHI
Onsdag 24 maj 2023 10:30 - 10:45 G2
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
Client-Side Application of Deep Learning Models through Teleradiology
Sébastien Jodogne
Onsdag 24 maj 2023 14:30 - 14:45 G4
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
Deep learning models for radiology are typically deployed either through cloud-based platforms, through on-premises infrastructures, or though heavyweight viewers. This tends to restrict the audience of deep learning models to radiologists working in state-of-the-art hospitals, which raises concerns about the democratization of deep learning for medical imaging, most notably in the context of research and education. We show that complex deep learning models can be applied directly inside Web browsers, without resorting to any external computation infrastructure, thanks to the use of WebAssembly, and we release our code as free and open-source software. This opens the path to the use of teleradiology solutions as an effective way to distribute, teach, and evaluate deep learning architectures.
Few-Shot Meta-Learning for Recognizing Facial Phenotypes of Genetic Disorders
Ömer Sümer
Onsdag 24 maj 2023 10:45 - 11:00 G2
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
Interpretable EEG-based Emotion Recognition using Fuzzy Cognitive Maps
Georgia Sovatzidi
Onsdag 24 maj 2023 14:15 - 14:30 G4
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
Random Forest and Gradient Boosted Trees for Patient Individualized Contrast Agent Dose Reduction in CT Angiography
René Pallenberg
Onsdag 24 maj 2023 11:15 - 11:30 G2
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
Reliability of IMU-Derived Gait Parameters in Foot Drop Patients
Armando Coccia
Onsdag 24 maj 2023 14:00 - 14:15 G4
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
Technological assessment of smart wearables and 5G-integrated edge computing for real-time health monitoring
Paulo Haas
Onsdag 24 maj 2023 14:45 - 15:00 G4
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
The Assessment of Glioblastoma Metabolic Activity via 11C-Methionine PET and Radiomics
Gleb Danilov
Onsdag 24 maj 2023 11:30 - 11:45 G2
MIE: Sensors, signals and Imaging Informatics, English, Förinspelat + På plats, Presentation, Avancerad
Towards Automated COVID-19 Presence and Severity Classification
Dominik Müller
Onsdag 24 maj 2023 10:15 - 10:30 G2
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
U-Net-based segmentation of current imaging biomarkers in OCT-scans of patients with age related macular degeneration
Mustafa Kemal Yildirim
Onsdag 24 maj 2023 11:00 - 11:15 G2
MIE: Sensors, signals and Imaging Informatics, English, Enbart på plats, Presentation, Avancerad
Age-related macular degeneration (AMD) is a major cause of blindness in the West. This study utilizes spectral domain optical coherence tomography (SD-OCT) for retinal imaging and deep learning for analysis. A convolutional neural network (CNN) was trained on 1300 expert-annotated SD-OCT scans to identify AMD biomarkers. Transfer learning improved the CNN's performance using weights from a separate classifier. The model accurately detects and segments AMD biomarkers in OCT scans, potentially assisting in patient prioritization and reducing ophthalmologists' workloads.