Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images.

Farhad Ghazvinian Zanjani,Svitlana Zinger,Bastian Piepers,Peter Schelkens,Saeed Mahmoudpour

doi:10.1117/1.jmi.6.2.027501

Abstract

The availability of massive amounts of data in histopathological whole-slide images (WSIs) has enabled the application of deep learning models and especially convolutional neural networks (CNNs), which have shown a high potential for improvement in cancer diagnosis. However, storage and transmission of large amounts of data such as gigapixel histopathological WSIs are challenging. Exploiting lossy compression algorithms for medical images is controversial but, as long as the clinical diagnosis is not affected, is acceptable. We study the impact of JPEG 2000 compression on our proposed CNN-based algorithm, which has produced performance comparable to that of pathologists and which was ranked second place in the CAMELYON17 challenge. Detecting tumor metastases in hematoxylin and eosin-stained tissue sections of breast lymph nodes is evaluated and compared with the pathologists' diagnoses in three different experimental setups. Our experiments show that the CNN model is robust against compression ratios up to 24:1 when it is trained on uncompressed high-quality images. We demonstrate that a model trained on lower quality images-i.e., lossy compressed images-depicts a classification performance that is significantly improved for the corresponding compression ratio. Moreover, it is also observed that the model performs equally well on all higher-quality images. These properties will help to design cloud-based computer-aided diagnosis (CAD) systems, e.g., telemedicine that employ deep CNN models that are more robust to image quality variations due to compression required to address data storage and transmission constraints. However, the results presented are specific to the CAD system and application described, and further work is needed to examine whether they generalize to other systems and applications.

Highlights

Computational histopathology involves computer-aided diagnosis (CAD) for microscopic analysis of stained histopathological whole-slide images (WSIs) to study the presence, localization, or grading of diseases
We investigate the impact of the compression ratio to evaluate the performance of a deep convolutional neural networks (CNNs) applied to JPEG 2000 compressed, histopathological WSI data
Since our CAD system exploits a learning model, we study the impact of degradation in image quality due to compression, both by varying the quality of the training data in the training phase as by varying the quality of the test data during the testing phase

Summary

Introduction

Computational histopathology involves computer-aided diagnosis (CAD) for microscopic analysis of stained histopathological whole-slide images (WSIs) to study the presence, localization, or grading of diseases. Emerging new scanners for digital microscopic imaging make it possible to acquire gigapixel histopathological images at a large scale.[1,2] These large-scale digital datasets make digital pathology a perfect use case to deploy data-greedy, deep-learning models. The availability of these massive amounts of data in combination with recent advances in artificial intelligence, based on state-of-the-art deep-learning models and convolutional neural networks (CNNs), results in a situation where for many clinical imageanalysis tasks, computational pathology solutions have a comparable performance to that of humans.[3] For example in pathology, recent deep learning-based techniques are comparable or even outperform humans in detecting and localizing breast cancer metastases in lymph node WSIs.[4].

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