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SotA/Methodology/Multiple Instance Learning

Multiple Instance Learning

13 benchmarks744 papers

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=x1,x2,…,xMX=\\{x_1,x_2, \ldots,x_M\\}X=x1​,x2​,…,xM​, and there is one single label YYY per bag, Y∈0,1Y\in\\{0, 1\\}Y∈0,1 in the case of a binary classification problem. It is assumed that individual labels y1,y2,…,yMy_1, y_2,\ldots, y_My1​,y2​,…,yM​ 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. On the other hand, a bag is positive, if at least one instance in the bag is positive.

<span class="description-source">Source: Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification </span>

Benchmarks

Multiple Instance Learning on CAMELYON16

AUCACCExpected Calibration ErrorFROCPatch AUC

Multiple Instance Learning on TCGA

ACCAUC

Multiple Instance Learning on Elephant

ACCAUC

Multiple Instance Learning on Musk v1

ACCAUC

Multiple Instance Learning on Musk v2

ACCAUC