HOPE: Histopathological image Organization and Processing Environment

Abstract

In disciplines such as digital pathology, the management of vast amounts of data, primarily ultra-high-resolution images, remains a significant barrier to the widespread adoption and seamless sharing of knowledge. Current research efforts are heavily focused on image encoding, often overlooking equally critical aspects such as indexing and efficient content transmission. Traditional compression methods, such as JPEG2000, prioritize reconstruction quality but do not inherently support direct retrieval or progressive transmission, both of which are essential for applications like telemedicine and large-scale digital pathology archives. To bridge this gap, we introduce a novel framework that integrates fractal compression, deep learning-based retrieval, and adaptive transmission, optimizing not only storage efficiency but also accessibility and scalability in histopathological imaging.

The Histopathological image Organization and Processing Environment (HOPE) framework here proposed exploits Partitioned Iterated Function Systems for image compression, achieving high compression ratios while preserving essential structural details. To mitigate the inherent artifacts of fractal compression, a U-Net autoencoder is integrated, refining decompressed images and enhancing visual quality. Additionally, a residual encoding mechanism is employed, allowing for lossless reconstruction when necessary. Unlike conventional methods, this framework enables direct retrieval from the compressed domain by extracting discriminative features from the fractal encoding coefficients. Another key innovation is its progressive transmission capability, which allows an initial low-bitrate preview to be sent, followed by incremental quality refinements based on diagnostic needs. This significantly reduces network load and enables real-time access to high-resolution histopathological images on resource-limited devices. Experimental results demonstrate that the proposed framework achieves compression performance comparable to JPEG2000, while simultaneously enabling efficient indexing, high-accuracy retrieval, and scalable transmission.