BS-Net is a new architecture designed to predict the severity of COVID-19 based on clinical data from different sources. This architecture uses four different blocks, which work together to estimate a six-valued score of the disease. This score is based on the interpretation of CXRs, which can be difficult and produce inter-rater variability among radiologists.
How BS-Net Works
The input image is processed using a convolutional backbone known as ResNet-18. Then, segmentation is performed usin
Overview: Co-Correcting for Medical Image Classification
Co-Correcting is a cutting-edge deep learning framework used for medical image classification. It was created to improve the accuracy of automated diagnosis and treatment processes in the medical field. When analyzing medical images, such as MRI scans or X-rays, accurately classifying them is vital for accurate diagnoses and care. The Co-Correcting framework does so by using a dual-network architecture, curriculum learning, and label corr
Medical image segmentation is an important task in the field of healthcare as it is used to identify and analyze the various structures present in the medical images, which can then be used to diagnose various diseases. UNETR, which stands for UNet Transformer, is an architecture for medical image segmentation that utilizes a pure transformer as the encoder to learn sequence representations of the input volume, thereby capturing the global multi-scale information more efficiently than other arch