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Attention-based multiple instance learning (AMIL) algorithms have proven tobe successful in utilizing gigapixel whole-slide images (WSIs) for a variety ofdifferent computational pathology tasks such as outcome prediction and cancersubtyping problems. We extended an AMIL approach to the task of survivalprediction by utilizing the classical Cox partial likelihood as a lossfunction, converting the AMIL model into a nonlinear proportional hazardsmodel. We applied the model to tissue microarray (TMA) slides of 330 lungcancer patients. The results show that AMIL approaches can handle very smallamounts of tissue from a TMA and reach similar C-index performance compared toestablished survival prediction methods trained with highly discriminativeclinical factors such as age, cancer grade, and cancer stage

Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays