November 16, 2016
MIA Meeting: https://youtu.be/sxkDzbtIJ5g?t=3039
Dayong Wang, Andy Beck
Beck Lab, Harvard Medical School at Beth Israel Deaconess Medical Center
Deep learning for computational pathology
Abstract: In this talk, we will provide an introduction to computational pathology, which is an emerging cross-discipline between pathology and computer engineering. Besides, we will introduce a deep learning-based automatic whole slide image analysis system for the identification of cancer metastases in breast sentinel lymph nodes. Our system won the 1st position in the International Challenge: Camelyon16, which was held at the International Symposium on Biomedical Imaging (ISBI) 2016. The system achieved an area under the receiver operating curve (AUC) of 0.925 for the task of whole slide image classification and an average sensitivity of 0.705 for the tumor localization task. A pathologist independently reviewed the same images, obtaining a whole slide image classification AUC of 0.966 and a tumor localization score of 0.733. By combining the predictions from the human pathologist and the automatic analysis system, the performance becomes even higher. These results demonstrate the power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses.
Babak Ehteshami Bejnordi
Beck Lab, Harvard Medical School at Beth Israel Deaconess Medical Center
Primer: Practical recommendations for training convolutional neural nets
Abstract: Deep learning, in particular convolutional neural network (ConvNet), is rapidly emerging as one of the most successful approaches for image and speech recognition. What distinguishes ConvNets and other deep learning systems from conventional machine learning techniques is their ability to learn the entire perception process from end to end. Deep learning systems use multiple nonlinear processing layers to learn useful representations of features directly from data. Searching the parameter space of deep architectures is a complex optimization task. ConvNets can be very sensitive to the setting of their hyper-parameters and network architecture setting. In this talk, I will give practical recommendations for training ConvNets and discuss the motivation and principles behind them. I will also provide recommendations on how to tackle various problems in analyzing medical image data such as lack of data, highly skewed class distributions, etc. Finally, I will introduce some of the advanced ConvNet architectures used in medical image analysis and their suitability for various tasks such as detection, classification, and segmentation.
For more information visit: https://www.broadinstitute.org/mia
Copyright Broad Institute, 2016. All rights reserved.
