A5067703063- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- AI in cancer detection
- Brain Tumor Detection and Classification
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neuroimaging Techniques and Applications
- Advanced Image Processing Techniques
- MRI in cancer diagnosis
- COVID-19 diagnosis using AI
- Medical Imaging and Analysis
- Photoacoustic and Ultrasonic Imaging
- Explainable Artificial Intelligence (XAI)
- Cerebrovascular and Carotid Artery Diseases
- Context-Aware Activity Recognition Systems
- Acute Ischemic Stroke Management
- Retinal Imaging and Analysis
- Ultrasound and Hyperthermia Applications
- Additive Manufacturing Materials and Processes
- Anomaly Detection Techniques and Applications
- Machine Learning in Healthcare
- Image Processing Techniques and Applications
- Cancer Genomics and Diagnostics
Otto-von-Guericke University Magdeburg
2019-2025
Human Technopole
2023-2025
A brain tumour is a mass or cluster of abnormal cells in the brain, which has possibility becoming life-threatening because its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis essential for successful treatment planning magnetic resonance imaging principal modality diagnostic tumours their extent. Deep Learning methods computer vision applications have shown significant improvement recent years, most can be credited fact that sizeable amount data...
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and challenged different sectors. One most effective ways to limit spread is early accurate diagnosing infected patients. Medical imaging, such as X-ray computed tomography (CT), combined potential artificial intelligence (AI), plays an essential role in supporting medical personnel diagnosis process. Thus, this article, five deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2,...
Abstract As vast histological archives are digitised, there is a pressing need to be able associate specific tissue substructures and incident pathology disease outcomes without arduous annotation. Here, we learn self-supervised representations using Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, can automatically segment into their constituent proportions...
Abstract Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tissue is exogenously heated to temperatures between 39 and 43 °C 60 min. Temperature monitoring can be performed non-invasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads motion artefacts images due movements patients during image acquisition. By discarding parts data, speed acquisition...
Blood vessels of the brain provide human with required nutrients and oxygen. As a vulnerable part cerebral blood supply, pathology small can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, Alzheimer's disease. With advancement 7 Tesla MRI systems, higher spatial image resolution be achieved, enabling depiction very in brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi's...
<title>Abstract</title> Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such acquisitions with temporal suffer from limited - also known as spatio-temporal trade-off dynamic MRI. Several approaches, including deep learning based super-resolution approaches by treating each time-point...
Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of nervous tissue, e.g. delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) high spatial resolution would play an important role visualising such tracts superior manner. However, obtaining image comes at expense longer scan time. Longer time associated with increase motion artefacts, due patient's psychological and...
Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning-based methods, in practice. One main reason for this is the black-box nature of these approaches and inherent problem missing insights automatically derived decisions. In order to increase trust paper presents that help interpret explain results learning algorithms by depicting anatomical areas influence decision algorithm most. Moreover, research a unified framework,...
Abstract Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models used. This seen growing popularity of ShapeNet (51,300 models) Princeton ModelNet (127,915 models). However, a large collection anatomical shapes (e.g., bones, organs, vessels) 3D surgical instruments missing. Methods We present MedShapeNet translate...
Neural networks, especially convolutional neural networks (CNN), are one of the most common tools these days used in computer vision. Most work with real-valued data using features. Complex-valued (CV-CNN) can preserve algebraic structure complex-valued input and have potential to learn more complex relationships between ground-truth. Although some comparisons CNNs CV-CNNs for different tasks been performed past, a large-scale investigation comparing models operating on has not conducted....
With the sudden growth of internet and digital documents available on web, task organizing text data has become a major problem.In recent times, classification one main techniques for data.The idea behind is to classify given piece predefined class or category.In present research work, SVM been used with linear kernel using One-V-Rest strategy.The trained various sets collected from sources.It may so happen that some particular words were not common around 5-6 years ago, but are currently...
Abstract The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and challenged different sectors. One most effective ways to limit is early accurate diagnosis infected patients. Medical imaging such as X-ray Computed Tomography (CT) combined potential Artificial Intelligence (AI) plays an essential role in supporting medical staff process. Thereby, five deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, DenseNet161) their Ensemble have been...
Prior to the deep learning era, shape was commonly used describe objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models used. This is seen numerous shape-related publications premier vision conferences as well growing popularity of ShapeNet (about 51,300 models) Princeton ModelNet (127,915 models). For domain, we present a large collection anatomical shapes...
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and challenged different sectors. One most effective ways to limit is early accurate diagnosing infected patients. Medical imaging, such as X-ray Computed Tomography (CT), combined potential Artificial Intelligence (AI), plays an essential role in supporting medical personnel diagnosis process. Thus, this article five deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 DenseNet161) their...
Abstract Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of living human brain mesoscopic level. From this, we can gain new insights into brain’s blood supply and vascular disease affecting small vessels. However, for quantitative characterization precise representation angioarchitecture to, example, inform blood-flow simulations, detailed segmentations smallest vessels are required. Given success deep...
Abstract Additive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure quality standards are met in all batch, X-ray computed tomography (X-CT) is often used combination with automated anomaly detection. For latter, deep learning (DL) detection techniques increasingly used, they can be trained to robust material being analysed and resilient poor image quality. Unfortunately, most recent popular DL models have...
In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospective or retrospective correction, have been proposed to avoid alleviate Recently, several other methods based on deep learning approaches solve this problem. This work proposes enhance performance existing models by inclusion additional information present image priors. The approach has shown promising results will...