With the new concept of attention approach 14, more papers have begun to use visual attention 15, 16, 17, being the first to use attention on medical images. However, the latter shows satisfactory results only on the single-pathology tasks. The first architectures to address this problem were CNN-RNN models from 12, 13. The latest surveys in the medical image captioning task 5, 11 offer a detailed description of domain knowledge from radiology and deep learning. Technical backgroundīecause image captioning is a multimodal problem, it draws a significant attention of both computer vision and natural language processing communities. They are often involved in treating cancer, heart diseases, stroke, blockages in the arteries and veins, fibroids in the uterus, back pains, liver and kidney problems. Interventional radiologists use radiology images to perform clinical procedures with minimally invasive techniques. They specialize on different parts of human body-breast imaging (mammograms), cardiovascular radiology (heart and circulatory system), chest radiology (heart and lungs), gastrointestinal radiology (stomach, intestines and abdomen), etc. Diagnostic radiologists interpret and report on images resulted from imaging procedures, diagnose the cause of patient’s symptoms, recommend treatment and offer additional clinical tests. They all use medical imaging procedures such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), nuclear medicine, positron emission tomography (PET) and ultrasound. There are three types of radiologists-diagnostic radiologists, interventional radiologists and radiation oncologists. Today, radiology actively implements new artificial intelligence approaches 8, 9, 10. Radiology is the medical discipline that uses medical imaging to diagnose and treat diseases. For that, we propose to adapt powerful models from non-medical domain. Providing automated support for this task has the potential to ease clinical workflows and improve both care quality and standardization. Automatic generation of chest X-ray medical reports using deep learning can assist and accelerate the diagnosis establishing process followed by clinicians. Thus, many healthcare systems outsource the medical image analysis task. In the COVID-19 era, there is a higher need for robust image captioning 5, 6, 7 framework. Among all, the qualification of radiologists as far as the correct diagnosis establishing should be stated as major problems. The typical manual annotation overload can lead to several problems, such as missed findings, inconsistent quantification, and delay of a patient’s stay in the hospital, which brings increased costs for the treatment. Having to study approximately 100 X-rays daily 5, radiologists are overloaded by the necessity to report their observations in writing, a tedious and time-consuming task that requires a deep domain-specific knowledge. Today, the generation of a free-text description based on clinical radiography results has become a convenient tool in clinical practice 5. Analyzing and interpreting X-ray images is especially crucial for diagnosing and monitoring a wide range of lung diseases, including pneumonia 2, pneumothorax 3, and COVID-19 complications 4. Out of the plethora of existing imaging modalities, X-ray remains one of the most widely-used visualization methods in many hospitals around the world, because it is inexpensive and easily accessible 1. Medical imaging is indispensable in the current diagnostic workflows.
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