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.

本文综述了使用整张切片图像训练深度神经网络的方法，并强调了这些方法背后不同的思想，以期解决整张切片图像所面临的挑战。

全切片图像分析的深度学习：概述

Deep Learning for Whole Slide Image Analysis: An Overview

We propose Neural Image Compression (NIC), a two-step method to build
convolutional neural networks for gigapixel image analysis solely using weak
image-level labels. First, gigapixel images are compressed using a neural
network trained in an unsupervised fashion, retaining high-level information
while suppressing pixel-level noise. Second, a convolutional neural network
(CNN) is trained on these compressed image representations to predict
image-level labels, avoiding the need for fine-grained manual annotations. We
compared several encoding strategies, namely reconstruction error minimization,
contrastive training and adversarial feature learning, and evaluated NIC on a
synthetic task and two public histopathology datasets. We found that NIC can
exploit visual cues associated with image-level labels successfully,
integrating both global and local visual information. Furthermore, we
visualized the regions of the input gigapixel images where the CNN attended to,
and confirmed that they overlapped with annotations from human experts.

本文提出了一种名为神经图像压缩（NIC）的两步方法来构建卷积神经网络，以仅使用弱的图片级标签对吉盘素级图像进行分析。