Lung cancer is the second most common type of cancer in the world. The reason for this staggering mortality rate is the near absolute absence of apparent symptoms in patients of early lung cancer. Consequently, the vast majority of lung cancers worldwide are diagnosed in stages III and IV, when the efficacy of existing treatments and hence the chances of survival are seriously compromised. Although deep learning methods have pushed forward automated early lung cancer diagnosis in recent years, all existing datasets and challenges seek to diagnose the disease using exclusively visual data. However, specialists also take into consideration all their knowledge of the context and the patient's medical history. In this project we aim at creating a method that includes both visual and clinical information for early stage lung cancer diagnosis.
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