Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning

Understand

Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks.

• Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level.
• We extend the application of CNNs to microscopy image classification and segmentation using multiple instance learning (MIL).
• We present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is robust to outliers.