Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
A review on AI-based medical image computing in head and neck surgery
Head and neck surgery is a fine surgical procedure with a complex anatomical space,
difficult operation and high risk. Medical image computing (MIC) that enables accurate and …
difficult operation and high risk. Medical image computing (MIC) that enables accurate and …
Structure boundary preserving segmentation for medical image with ambiguous boundary
In this paper, we propose a novel image segmentation method to tackle two critical problems
of medical image, which are (i) ambiguity of structure boundary in the medical image domain …
of medical image, which are (i) ambiguity of structure boundary in the medical image domain …
Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task FCN
Accurate and automatic segmentation of individual tooth is critical for computer-aided
analysis towards clinical decision support and treatment planning. Three-dimensional …
analysis towards clinical decision support and treatment planning. Three-dimensional …
Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: a multicenter multivendor study
AS Fahmy, U Neisius, RH Chan, EJ Rowin… - Radiology, 2020 - pubs.rsna.org
Background Cardiac MRI late gadolinium enhancement (LGE) scar volume is an important
marker for outcome prediction in patients with hypertrophic cardiomyopathy (HCM); …
marker for outcome prediction in patients with hypertrophic cardiomyopathy (HCM); …
Block level skip connections across cascaded V-Net for multi-organ segmentation
Multi-organ segmentation is a challenging task due to the label imbalance and structural
differences between different organs. In this work, we propose an efficient cascaded V-Net …
differences between different organs. In this work, we propose an efficient cascaded V-Net …
Craniomaxillofacial bony structures segmentation from MRI with deep-supervision adversarial learning
Automatic segmentation of medical images finds abundant applications in clinical studies.
Computed Tomography (CT) imaging plays a critical role in diagnostic and surgical planning …
Computed Tomography (CT) imaging plays a critical role in diagnostic and surgical planning …
Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy
FE Fernandes Jr, GG Yen - Information Sciences, 2021 - Elsevier
Abstract Deep Convolutional Neural Networks (DCNNs) have the potential to revolutionize
the field of Medical Imaging Diagnostics due to their capabilities of learning by using only …
the field of Medical Imaging Diagnostics due to their capabilities of learning by using only …
One-shot generative adversarial learning for MRI segmentation of craniomaxillofacial bony structures
Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of
craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However …
craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However …
H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images
A Pedersen, E Smistad, TV Rise, VG Dale… - Frontiers in …, 2022 - frontiersin.org
Over the past decades, histopathological cancer diagnostics has become more complex,
and the increasing number of biopsies is a challenge for most pathology laboratories. Thus …
and the increasing number of biopsies is a challenge for most pathology laboratories. Thus …