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Chest diseases being common, may also require a radiological diagnosis when the symptoms are critical. This calls for urgent tests, results and their diagnosis. From printing out the chest X-rays to generating a report is a time-consuming process. The time period may even be of criticality if the disease is serious and life-threatening. Sometimes it may even happen that even the most experienced doctor may miss some details and give a wrong diagnosis. In reallife what people do in such cases is they take a second opinion, which again takes a lot of precious time, which could've been used in proceeding with the treatment. Our solution aims at precision and quickness of this process.
A pytorch implementation of On the Automatic Generation of Medical Imaging Reports.
Identifying diseases in chest X-rays using convolutional neural networks
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