Figure 3.

Summary of different aggregating methods for multiple lesion analyses. (A) Defining a fixed number of a lesion to analyze for each patient. (B) Aggregating lesions into one volume (tumor burden). (C) Aggregating features extracted from several lesions (ie, using metrics such as the average value or the minimal value). (D) Predicting lesion-level response then aggregating the predictions to assess patient outcomes. (E and F) Assigning for each lesion the patient outcome to predict then using predefined aggregation metrics (E) or learned aggregation methods (attention) in multiple-instance learning approaches (F).