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Deep Learning (DL) requires a large amount of training data to providequality outcomes. However, the field of medical imaging suffers from the lackof sufficient data for properly training DL models because medical imagesrequire manual labelling carried out by clinical experts thus the process istime-consuming, expensive, and error-prone. Recently, transfer learning (TL)was introduced to reduce the need for the annotation procedure by means oftransferring the knowledge performed by a previous task and then fine-tuningthe result using a relatively small dataset. Nowadays, multiple classificationmethods from medical imaging make use of TL from general-purpose pre-trainedmodels, e.g., ImageNet, which has been proven to be ineffective due to themismatch between the features learned from natural images (ImageNet) and thosemore specific from medical images especially medical gray images such asX-rays. ImageNet does not have grayscale images such as MRI, CT, and X-ray. Inthis paper, we propose a novel DL model to be used for addressingclassification tasks of medical imaging, called MedNet. To do so, we aim toissue two versions of MedNet. The first one is Gray-MedNet which will betrained on 3M publicly available gray-scale medical images including MRI, CT,X-ray, ultrasound, and PET. The second version is Color-MedNet which will betrained on 3M publicly available color medical images including histopathology,taken images, and many others. To validate the effectiveness MedNet, bothversions will be fine-tuned to train on the target tasks of a more reduced setof medical images. MedNet performs as the pre-trained model to tackle anyreal-world application from medical imaging and achieve the level ofgeneralization needed for dealing with medical imaging tasks, e.g.classification. MedNet would serve the research community as a baseline forfuture research.

MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks