PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessing
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917067458871296 |
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| author | Verghese, Gregory Baptista, Anthony Eke, Chima Rafique, Holly Li, Mengyuan Mohamed, Fathima Bhalla, Ananya Ryan, Lucy Pitcher, Michael Parisini, Enrico Piazzese, Concetta Ing-Simmons, Liz Grigoriadis, Anita |
| author_facet | Verghese, Gregory Baptista, Anthony Eke, Chima Rafique, Holly Li, Mengyuan Mohamed, Fathima Bhalla, Ananya Ryan, Lucy Pitcher, Michael Parisini, Enrico Piazzese, Concetta Ing-Simmons, Liz Grigoriadis, Anita |
| contents | The integration of artificial intelligence (AI) into pathology is advancing precision medicine by improving diagnosis, treatment planning, and patient outcomes. Digitised whole-slide images (WSIs) capture rich spatial and morphological information vital for understanding disease biology, yet their gigapixel scale and variability pose major challenges for standardisation and analysis. Robust preprocessing, covering tissue detection, tessellation, stain normalisation, and annotation parsing is critical but often limited by fragmented and inconsistent workflows. We present PySlyde, a lightweight, open-source Python toolkit built on OpenSlide to simplify and standardise WSI preprocessing. PySlyde provides an intuitive API for slide loading, annotation management, tissue detection, tiling, and feature extraction, compatible with modern pathology foundation models. By unifying these processes, it streamlines WSI preprocessing, enhances reproducibility, and accelerates the generation of AI-ready datasets, enabling researchers to focus on model development and downstream analysis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_05183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessing Verghese, Gregory Baptista, Anthony Eke, Chima Rafique, Holly Li, Mengyuan Mohamed, Fathima Bhalla, Ananya Ryan, Lucy Pitcher, Michael Parisini, Enrico Piazzese, Concetta Ing-Simmons, Liz Grigoriadis, Anita Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing The integration of artificial intelligence (AI) into pathology is advancing precision medicine by improving diagnosis, treatment planning, and patient outcomes. Digitised whole-slide images (WSIs) capture rich spatial and morphological information vital for understanding disease biology, yet their gigapixel scale and variability pose major challenges for standardisation and analysis. Robust preprocessing, covering tissue detection, tessellation, stain normalisation, and annotation parsing is critical but often limited by fragmented and inconsistent workflows. We present PySlyde, a lightweight, open-source Python toolkit built on OpenSlide to simplify and standardise WSI preprocessing. PySlyde provides an intuitive API for slide loading, annotation management, tissue detection, tiling, and feature extraction, compatible with modern pathology foundation models. By unifying these processes, it streamlines WSI preprocessing, enhances reproducibility, and accelerates the generation of AI-ready datasets, enabling researchers to focus on model development and downstream analysis. |
| title | PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessing |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2511.05183 |