Abstract Multiple Instance Learning (MIL) has been widely applied in pathology towards
solving critical problems such as automating cancer diagnosis and grading, predicting …

Multiple instance learning is an ideal mode of analysis for histopathology data, where vast
whole slide images are typically annotated with a single global label. In such cases, a whole …

Multiple instance (MI) learning with a convolutional neural network enables end-to-end
training in the presence of weak image-level labels. We propose a new method for …

Analyzing high resolution whole slide images (WSIs) with regard to information across
multiple scales poses a significant challenge in digital pathology. Multi-instance learning …

Multiple instance learning (MIL) is a variation of supervised learning where a single class
label is assigned to a bag of instances. In this paper, we state the MIL problem as learning …

The classification of gigapixel histopathology images with deep multiple instance learning
models has become a critical task in digital pathology and precision medicine. In this work …

Multiple instance learning (MIL) is a variation of supervised learning where a single class
label is assigned to a set of instances, eg, image patches. After providing a comprehensive …

Abstract Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract
discriminative feature representations within Whole Slide Images (WSIs) in computational …

Learning informative representations is crucial for classification and prediction tasks on
histopathological images. Due to the huge image size, whole-slide histopathological image …

When applying multi-instance learning (MIL) to make predictions for bags of instances, the
prediction accuracy of an instance often depends on not only the instance itself but also its …