DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over the time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches

Tonsil Cell Products which Modify in Vitro Proliferation of Blood Lymphocytes

Human palatine tonsil lymphocytes, when compared to peripheral blood lymphocytes (PBL), were in an activated state even though there was no in vitro stimulation. When these tonsil lymphocytes were cultured in the absence of serum and polyclonal mitogens or antigens, the supernatant fluid often inhibited the proliferative response of target PBL to con A. The extent of this suppression ranged from 22% to 84%, and target cell viability was 90% or greater. There was no evidence for the presence of immunoglobulins or (alpha)2-macroglobulin in whole supernatant fluids. The suppressor was partially denatured at 80(DEGREES)C and was rendered completely inactive upon exposure to 100(DEGREES)C for 5 min. It was trypsin sensitive, and had an apparent molecular weight of 100,000 or greater. The protein adhered strongly to DE-52 cellulose, and the most active material eluted with 0.4-0.6 M NaCl. The suppressor was active in the pH range 5.0 (+OR-) 0.6 as demonstrated by isoelectric focusing. Occasionally, supernatant fluids comprised material which augmented the expected response of con A stimulated PBL. The augmentor was 30,000 in molecular weight and was eluted from DE-52 cellulose in the 0.15-0.25 M NaCl range. Nearly all supernatant preparations tested contained a mitogenic substance which stimulated naive allogeneic human PBL without the necessity of co-stimulation by a mitogen. The mitogenic factor (MF) behaved in a dose dependent fashion and was evidently different from the augmentor since the MF stimulated PBL independently of lectin co-stimulation

AutoML Systems For Medical Imaging

The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.Comment: 11 pages, 4 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

COVID-19 detection using chest X-ray images based on Machine learning and Deep learning models: Further evidence from Data Augmentation

In this master's thesis, deep learning and machine learning are applied to chest X-ray images to detect COVID-1