{"url":"/sota/multiple-instance-learning-on-tcga","task":{"name":"Multiple Instance Learning","url":"/task/multiple-instance-learning","note":null},"dataset":{"name":"TCGA","url":"/dataset/tcga"},"category":"Methodology","categories":["Methodology"],"category_note":null,"description":"**Multiple Instance Learning** is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\\\\{x_1,x_2, \\ldots,x_M\\\\}$, and there is one single label $Y$ per bag, $Y\\in\\\\{0, 1\\\\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. 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