  Attention-based multiple instance learning (AMIL) algorithms have proven to
be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of
different computational pathology tasks such as outcome prediction and cancer
subtyping problems. We extended an AMIL approach to the task of survival
prediction by utilizing the classical Cox partial likelihood as a loss
function, converting the AMIL model into a nonlinear proportional hazards
model. We applied the model to tissue microarray (TMA) slides of 330 lung
cancer patients. The results show that AMIL approaches can handle very small
amounts of tissue from a TMA and reach similar C-index performance compared to
established survival prediction methods trained with highly discriminative
clinical factors such as age, cancer grade, and cancer stage
