Magnifying Networks for Images with Billions of Pixels

The shift towards end-to-end deep learning has brought unprecedented advances in many areas of computer vision. However, there are cases where the input images are excessively large, deeming end-to-end approaches impossible. In this paper, we introduce a new network, the Magnifying Network (MagNet), which can be trained end-to-end independently of the input image size. MagNets combine convolutional neural networks with differentiable spatial transformers, in a new way, to navigate and successfully learn from images with billions of pixels. Drawing inspiration from the magnifying nature of an ordinary brightfield microscope, a MagNet processes a downsampled version of an image, and without supervision learns how to identify areas that may carry value to the task at hand, upsamples them, and recursively repeats this process on each of the extracted patches. Our results on the publicly available Camelyon16 and Camelyon17 datasets first corroborate to the effectiveness of MagNets and the proposed optimization framework and second, demonstrate the advantage of Magnets' built-in transparency, an attribute of utmost importance for critical processes such as medical diagnosis.

A New Look at Ghost Normalization

Batch normalization (BatchNorm) is an effective yet poorly understood technique for neural network optimization. It is often assumed that the degradation in BatchNorm performance to smaller batch sizes stems from it having to estimate layer statistics using smaller sample sizes. However, recently, Ghost normalization (GhostNorm), a variant of BatchNorm that explicitly uses smaller sample sizes for normalization, has been shown to improve upon BatchNorm in some datasets. Our contributions are: (i) we uncover a source of regularization that is unique to GhostNorm, and not simply an extension from BatchNorm, (ii) three types of GhostNorm implementations are described, two of which employ BatchNorm as the underlying normalization technique, (iii) by visualising the loss landscape of GhostNorm, we observe that GhostNorm consistently decreases the smoothness when compared to BatchNorm, (iv) we introduce Sequential Normalization (SeqNorm), and report superior performance over state-of-the-art methodologies on both CIFAR--10 and CIFAR--100 datasets.

Deep Learning for Whole Slide Image Analysis: An Overview

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artefacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.

Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in the field of oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, amongst numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole digitized haematoxylin & eosin (H&E) stained histopathology slides. Using a real-world data set we demonstrate the effectiveness of the method and present a detailed analysis of its different elements which corroborate its ability to extract and learn salient, discriminative, and clinically meaningful content.