Streaming convolutional neural networks for end-to-end learning with multi-megapixel images

TL;DR

This paper introduces a novel streaming CNN method that enables end-to-end training on multi-megapixel images by processing smaller tiles, significantly improving performance in medical imaging tasks.

Contribution

The authors propose a new approach for training CNNs on large images directly, overcoming memory constraints and demonstrating improved accuracy on medical imaging datasets.

Findings

Achieved 66-megapixel image processing with 50GB memory savings.

Improved metastasis detection AUC from 0.580 to 0.706.

Enhanced correlation metric from 0.485 to 0.570 with increased resolution.

Abstract

Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision contain images much less than a megapixel in size (0.09MP for ImageNet and 0.001MP for CIFAR-10). In some domains such as medical imaging, multi-megapixel images are needed to identify the presence of disease accurately. We propose a novel method to directly train convolutional neural networks using any input image size end-to-end. This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image. In this work, we show a proof of concept using images of up to 66-megapixels (8192x8192), saving approximately 50GB of memory per image. Using two public challenge datasets, we demonstrate that CNNs can learn to extract relevant information from these large images and benefit from increasing resolution. We improved the area under the receiver-operating characteristic curve from 0.580 (4MP) to 0.706 (66MP) for metastasis detection in breast cancer (CAMELYON17). We also obtained a Spearman correlation metric approaching state-of-the-art performance on the TUPAC16 dataset, from 0.485 (1MP) to 0.570 (16MP). Code to reproduce a subset of the experiments is available at https://github.com/DIAGNijmegen/StreamingCNN.