Radiological sciences in the last ten years have advanced in a revolutionary manner, especially when it comes about medical imaging and computerized medical image processing. These techniques help in the understanding of the disease as well as initiation and evaluation of ongoing treatment. Apart from this, the dataset of these images is used in further analysis of such diseases occurring around the world as a whole. Heather Landi, a senior editor at Fierce Healthcare, writes in an article that IBM researchers estimate that medical images, as the largest and fastest-growing data source in the healthcare industry, account for at least 90 percent of all medical data. We can use a computer to process and manipulate the multidimensional digital images of psychological structures in order to visualize hidden characteristic diagnostic features that are very difficult or perhaps impossible to see using planer imaging methods.
Pulmonology is a field of medicine that deals with respiratory tract diseases, and the medical imaging used by pulmonologists is predominantly chest imaging: CXR, CT, MRI, PET, V/Q scanning, ultrasound, and the like. High-quality medical image analysis is crucial in pulmonary diagnostics and treatment. While the most conventional method of assessing lung tissue and surrounding structures is computed tomography, other types are used for additional insights and to accommodate individual contraindications. When acquiring a medical image, radiologists have to strike the balance between quality and the permissible degree of exposure for the patient. This is especially critical when a person has to get multiple chest scans in a short period of time.
"Deep Learning Is Setting Records!!" There is tremendous growth in people searching or showing interests about deep learning & AI in last few years. Every day hundreds of new articles get published on it in social media & press media. Above chart broadly explains as why search trend is ever growing for deep learning & AI. Fundamentally deep learning is a subset of Machine Learning. The reason as why it is exciting is that more data you give to deep learning usually you get more accuracy out from the model.
IMAGE: At train-time, we embed each pixel of the ground truth image SI as the mean of predefined guide functions f over instance pixels it belongs to, resuling in embeddings e(S,... view more Skoltech researchers have presented a new biological image processing method that accurately picks out specific biological objects in complex images. Their results will be presented as an oral talk at the high-profile computer vision conference, CVPR 2020. Biologists get a wealth of information in the form of biological images, which makes their automatic processing a formidable task. Researchers often have to handle images with a large number of objects, which is especially hard when it comes to microscopy images with overlapping objects and poor image quality and sharpness. Machine Learning (ML) helps train the computer to process biological images, making data analysis much faster, more accurate and consistent across experiments.
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source, instead of Gaussian noise that dominates in photography. To get clean fluorescence microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise fluorescence microscopy images. While such algorithms exist, there are no such datasets available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use imaging averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.