Search Results for author:
Found 15 papers, 4 papers with code
Parameter choices in HaarPSI for IQA with medical images
So far, these parameters have been aligned for natural images.
A study on the adequacy of common IQA measures for medical images
Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images.
A study of why we need to reassess full reference image quality assessment with medical images
Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on medical images.
Full reference image quality assessment
Full-Reference Image Quality Assessment
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Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry
We used a long short-term memory network to implement a wavelength-flexible network architecture and proposed the Jensen-Shannon divergence to predict the most suitable training dataset.
Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms
We show that training on simulated data results in artefacts and biases in the estimates, reinforcing the existence of a domain gap between simulation and experiment.
Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Synthetic medical image generation has evolved as a key technique for neural network training and validation.
Semantic segmentation of multispectral photoacoustic images using deep learning
Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.
Photoacoustic image synthesis with generative adversarial networks
Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution.
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation.
Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern
Methods: In this paper, we present a novel approach to 3D reconstruction of PAT data (Tattoo tomography) that does not require an external tracking system and can smoothly be integrated into clinical workflows.
Deep learning for biomedical photoacoustic imaging: A review
Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications.
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Image-based tracking of medical instruments is an integral part of surgical data science applications.
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed.
Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
In this work, we tackle the challenge by employing learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI) to obtain quantitative estimates for blood oxygenation.
Context encoding enables machine learning-based quantitative photoacoustics
Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases.
