Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.

Since its introduction in 2015, the U-Net architecture used in Deep Learning has played a crucial role in medical imaging. Recognized for its ability to accurately discriminate small structures, the U-Net has received more than 2600 citations in academic literature, which motivated continuous enhancements to its architecture. In hospitals, chest radiography is the primary diagnostic method for pulmonary disorders, however, accurate lung segmentation in chest X-ray images remains a challenging task, primarily due to the significant variations in lung shapes and the presence of intense opacities caused by various diseases.

Abstract ID: 103 GAMOS: Implementation of a graphical user interface for dosimetry calculation in radiotherapy.

There are several computer programs or combination of programs for radiation tracking and other information in tissues by using Monte Carlo simulation [1]. Among these are GEANT4 [2] programs provided as classes that can be incorporated in C++ codes to achieve different tasks in radiation interactions with matter. GEANT4 made the physics easier but requires often a long learning-curve that implies a good knowledge of C++ and the Geant4 architecture.