# Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning Source: [Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:00:51.301Z Authors: Oren Z. Kraus, Lei Jimmy Ba, Brendan Frey Published: 2015-11-17T06:55:58.000Z ## Abstract 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. Combining CNNs with MIL enables training CNNs using full resolution microscopy images with global labels. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. We show that training MIL CNNs end-to-end outperforms several previous methods on both mammalian and yeast microscopy images without requiring any segmentation steps. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()