Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research
… for a series of preliminary pre-processing steps prior to training… image modifications should
be imitated as potential image … in radiology addressed with deep neural networks mainly …
be imitated as potential image … in radiology addressed with deep neural networks mainly …
A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images
… as a dangerous infectious disease caused by the SARS-CoV-… key changes that can be seen
in patient chest X-ray and CT-… training amenities, and address data overfitting concerns. In …
in patient chest X-ray and CT-… training amenities, and address data overfitting concerns. In …
Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review
B Rusanov, GM Hassan, M Reynolds, M Sabet… - Medical …, 2022 - Wiley Online Library
… For example, early attempts at modeling scatter from primary signals in radiographic … ,
cycle-consistency becomes problematic where large changes to the output are desired, 25 such as …
cycle-consistency becomes problematic where large changes to the output are desired, 25 such as …
On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise
… % of testing data were successfully altered by at least once. The … To study if the robustness
is caused by the specific network … . To address the imbalanced malignant (345) and benign (…
is caused by the specific network … . To address the imbalanced malignant (345) and benign (…
Deep learning–based metal artefact reduction in PET/CT imaging
… To address this issue, a number of metal artefact reduction (… of CT images from the
unaffected or preliminary corrected … images illustrate clearly the degree of PET signal alteration …
unaffected or preliminary corrected … images illustrate clearly the degree of PET signal alteration …
Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT
R Singh, SR Digumarthy, VV Muse… - American Journal of …, 2020 - Am Roentgen Ray Soc
… noise and signal in CT images and to enhance signal while … It makes changes in the projection
data domain to improve SNR … Inclusion of these vendor-naïve radiologists helped address …
data domain to improve SNR … Inclusion of these vendor-naïve radiologists helped address …
A deep learning approach for liver and tumor segmentation in CT images using ResUNet
… the ResNet and UNet models to address this gap. The two … liver and lesion tissue caused
by various acquisition methods, … , particularly when texture changes differentiate lesions and …
by various acquisition methods, … , particularly when texture changes differentiate lesions and …
Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement
… a fully data-driven mapping from measured signal to images. … directions to address these
challenges are presented. … Cornerstone works in CT and MRI have shown deep learning can …
challenges are presented. … Cornerstone works in CT and MRI have shown deep learning can …
Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative …
L Zeng, X Xu, W Zeng, W Peng, J Zhang… - European Journal of …, 2021 - Elsevier
… To address this issue, various methods have been proposed … This is builded on our preliminary
work [24], with a network … have different receptivity to image texture changes, which may …
work [24], with a network … have different receptivity to image texture changes, which may …
Deep learning–based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses
… which can be affected greatly by slight changes in the image as well as interreader variability
(… features, which are sensitive to changes in image spatial and density resolutions (11). Our …
(… features, which are sensitive to changes in image spatial and density resolutions (11). Our …