Computer Vision News - July 2022

45 Vision Transformers in Medical CV as the error can back-propagate frommultiple paths. Finally, two fully connected layers are adopted with one dropout layer between them. SoftMax is employed to finalize the output. In total, MedNet consists of 44 convolutional layers. Previous networks are as important, as it can be seen in the figure below on a very high- level description (not including the individual layers). The proposed is a novel technique for translation using transfer learning to overcome the issues of translation from a pre-trained model of ImageNet. The technique is used in medical imaging applications and helps to address the lack of training data. The approach is to perform training on a model called Gray-MedNet with publicly available data in different medical fields, including CT, MRI, PET, histology, and so on. It is also based on training a model called Color-MedNet with publicly available data in different medical fields, including CT, MRI, PET, histology, and so on. These datasets include: CT images (abdomen, bladder, brain, chest, kidney, cervix, breast), MRI (neuroimaging, cardiovascular, liver, functional, oncology, phase contrast), PET ( cardiology, infected tissues, neuroimaging, oncology, musculoskeletal, pharmacokinetics), histology(epithelium,endothelium,mesenchyme,bloodcells,neurons, germcells,placenta), X-Rays ( radiography, mammography, fluoroscopy, contrast radiography, arthrography, discography, dexa Scan), Ultrasound (breast, doppler, abdominal, transabdominal, cranial, spleen, transrectal), ophthalmic fundus images, corneal topographic maps, skin cancer images, and many more. The first step is to take data-augmentation techniques and use them to address the imbalance issue, followed using generative adversarial networks to train the models. This is done through the collection of data and filtering of information, followed. First,MedNet is trained and validated on five small target datasets to check the effectiveness of MedNet. One important aspect, considering open science, which those days it becomes more and more prominent is that MedNet and its source code will be available in the research community to be specifically trained for a particular application.