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Processing histopathological whole slide images (WSI) leads to massive storage requirements for clinics worldwide. Even after lossy image compression during image acquisition, additional lossy compression is frequently possible without substantially affecting the performance of deep learning-based (DL) downstream tasks. In this paper, we show that the commonly used JPEG algorithm is not best suited for further compression and we propose stain quantized latent compression (SQLC), a novel DL based histopathology data compression approach.

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Abstract: Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution

  • 会议论文

  • 发表日期：

    2025年

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

摘要

Processing histopathological whole slide images (WSI) leads to massive storage requirements for clinics worldwide. Even after lossy image compression during image acquisition, additional lossy compression is frequently possible without substantially affecting the performance of deep learning-based (DL) downstream tasks. In this paper, we show that the commonly used JPEG algorithm is not best suited for further compression and we propose stain quantized latent compression (SQLC), a novel DL based histopathology data compression approach.