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 …

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

Digital whole slides images contain an enormous amount of information providing a strong
motivation for the development of automated image analysis tools. Particularly deep neural …

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 …

Motivation Multiple instance learning (MIL) is a powerful technique to classify whole slide
images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to …

Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple
Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the …

Multiple-instance learning (MIL) is an attractive approach for digital pathology applications
as it reduces the costs related to data collection and labelling. However, it is not clear how …

Whole slide image (WSI) classification is a critical task in computational pathology, requiring
the processing of gigapixel-sized images, which is challenging for current deep-learning …

In the field of computational pathology, the use of decision support systems powered by
state-of-the-art deep learning solutions has been hampered by the lack of large labeled …