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Recent image-based survival models rely on discriminative patch labeling, which are both time consuming and infeasible to extend to large scale cancer datasets. Different from the existing works on learning using key patches or clusters from WSIs, we take advantages of a deep multiple instance learning to encode all possible patterns from WSIs and consider the joint effects from different patterns for clinical outcomes prediction. We evaluate our model in its ability to predict patients’ survival risks across the Lung and Brain tumors from two large whole slide pathological images datasets. The proposed framework can improve the prediction performances compared with existing state-of-the-arts survival analysis approaches. Results also demonstrate the effectiveness of the proposed method as a recommender system to provide personalized recommendations based on an individual’s calculated risk.