In the advent of a digital health revolution, vast amounts of clinical data are being generated,
stored and processed on a daily basis. This has made the storage and retrieval of large …

With the ongoing development of machine learning techniques, it is now necessary to train
and evaluate these algorithms to have access to high-quality medical X-ray datasets. This …

As scanners produce higher-resolution and more densely sampled images, this raises the
challenge of data storage, transmission and communication within healthcare systems …

Over the preceding decade, there has been a discernible surge in the prominence of
artificial intelligence, marked by the development of various methodologies, among which …

In compression of medical image using convolutional neural network trained with the back-
propagation algorithm and lefted wavelet transformation is proposed to compress high …

As a speciality, radiology produces the highest volume of medical images in clinical
establishments compared to other commonly employed imaging modalities like digital …

Image compression plays a crucial role in the field of medical imaging, including Magnetic
Resonance Imaging (MRI). The MRI images are typically large and high-resolution, which …

Early works on medical image compression date to the 1980's with the impetus on
deployment of teleradiology systems for high-resolution digital X-ray detectors …

We propose a convolutional neural network (CNN) based image compression scheme that
is compatible with JPEG-2000. Specifically, our scheme reuses the existing JPEG-2000 …

Modern imaging modalities generate large volumes of medical data that place a heavy
burden on both storage and transmission. Consequently, image data compression is a key …