PET/CT imaging is the gold standard for the diagnosis and staging of lung cancer. However, especially in healthcare systems with limited resources, costly PET/CT images are often not readily available. Conventional machine learning models either process CT or PET/CT images but not both. Models designed for PET/CT images are hence restricted by the number of PET images, such that they are unable to additionally leverage CT-only data. In this work, we apply the concept of visual soft attention to efficiently learn a model for lung cancer segmentation from only a small fraction of PET/CT scans and a larger pool of CT-only scans. We show that our model is capable of jointly processing PET/CT as well as CT-only images, which performs on par with the respective baselines whether or not PET images are available at test time. We then demonstrate that the model learns efficiently from only a few PET/CT scans in a setting where mostly CT-only data is available, unlike conventional models.
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