AIMC Topic: Image Processing, Computer-Assisted

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Efficient Deep Learning-based Wound-bed Segmentation For Mobile Applications.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper proposes a deep learning image segmentation method for the purpose of segmenting wound-bed regions from the background. Our contributions include proposing a fast and efficient convolutional neural networks (CNN)-based segmentation network...

UENet: A Novel Generative Adversarial Network for Angiography Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Convolutional neural networks (CNNs) have been widely used in medical image segmentation. Vessel segmentation in coronary angiography remains a challenging task. It is a great challenge to extract fine features of coronary artery for segmentation due...

Multiple Slice k-space Deep Learning for Magnetic Resonance Imaging Reconstruction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Magnetic resonance imaging (MRI) has been one of the most powerful and valuable imaging methods for medical diagnosis and staging of disease. Due to the long scan time of MRI acquisition, k-space under-samplings is required during the acquisition pro...

Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Ki-67 labelling index is a biomarker which is used across the world to predict the aggressiveness of cancer. To compute the Ki-67 index, pathologists normally count the tumour nuclei from the slide images manually; hence it is timeconsuming and is su...

Cardiac Chamber Segmentation Using Deep Learning on Magnetic Resonance Images from Patients Before and After Atrial Septal Occlusion Surgery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose a robust technique for segmenting magnetic resonance images of post-atrial septal occlusion intervention in the cardiac chamber. The technique can be used to determine the surgical outcomes of atrial septal defects before and after implant...

AI in Medical Imaging Informatics: Current Challenges and Future Directions.

IEEE journal of biomedical and health informatics
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in me...

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Brain : a journal of neurology
Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a compr...

Application of deep learning and image processing analysis of photographs for amblyopia screening.

Indian journal of ophthalmology
PURPOSE: Photo screeners and autorefractors have been used to screen children for amblyopia risk factors (ARF) but are limited by cost and efficacy. We looked for a deep learning and image processing analysis-based system to screen for ARF.

Explainable AI (xAI) for Anatomic Pathology.

Advances in anatomic pathology
Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of WSI systems as class II medical devices. In response to new market forces and recent technology advances outside of pathology, a new field of computat...