Abstract: Deep-learning on Lossily Compressed Pathology Images Adverse Effects for ImageNet Pre-trained Models

Maximilian Fischer, Peter Neher, Michael Götz, Shuhan Xiao, Silvia Dias Almeida, Peter Schüffler, Alexander Muckenhuber, Rickmer Braren, Jens Kleesiek, Marco Nolden, Klaus Maier-Hein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Digital whole slide imaging (WSI) systems allow scanning complete probes at microscopic resolutions, making image compression inevitable to reduce storage costs. While lossy image compression is readily incorporated in proprietary file formats as well as the open DICOM format for WSI, its impact on deep-learning algorithms is largely unknown. We compare the performance of several deep learning classification architectures on different datasets using a wide range and different combinations of compression ratios during training and inference. We use ImageNet pre-trained models, which is commonly applied in computational pathology. With this work, we present a quantitative assessment on the effects of repeated lossy JPEG compression for ImageNet pre-trained models. We show adverse effects for a classification task, when certain quality factors are combined during training and inference. This work was published on the International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis [1].

Original languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2023 Proceedings, German Workshop on Medical Image Computing, Braunschweig
EditorsThomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages1
ISBN (Print)9783658416560
StatePublished - 2023
EventBildverarbeitung für die Medizin Workshop, BVM 2023 - Braunschweig, Germany
Duration: 2 Jul 20234 Jul 2023

Publication series

NameInformatik aktuell
ISSN (Print)1431-472X


ConferenceBildverarbeitung für die Medizin Workshop, BVM 2023


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