ArticleFrontiers in public health2022
A Comparison Between Single- and Multi-Scale Approaches for Classification of Histopathology Images.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- PathScaleBench: A Multidimensional Benchmark of Cross-Scale Representation Behavior and Magnification-Shift Robustness in Pathology Foundation Models.Journal of imaging informatics in medicine · 2026Article
- Article
- Oral squamous cell carcinoma diagnosis: an interpretable deep learning approach using multi-scale pathological images.BMC oral health · 2026Article
- Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.Diagnostics (Basel, Switzerland) · 2026Article
- Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer.BMC medical imaging · 2025Article
- Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review.Journal of pathology informatics · 2024Review
- Zoom Pattern Signatures for Fetal Ultrasound Structures.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.Nature communications · 2024Article
- SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images.EBioMedicine · 2024Article
- Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma.The American journal of pathology · 2023Article
- Improving Representation Learning for Histopathologic Images with Cluster Constraints.Proceedings. IEEE International Conference on Computer Vision · 2023Article
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3 authors.
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Abstract
Whole slide images (WSIs) are digitized histopathology images. WSIs are stored in a pyramidal data structure that contains the same images at multiple magnification levels. In digital pathology, most algorithmic approaches to analyze WSIs use a single magnification level. However, images at different magnification levels may reveal relevant and distinct properties in the image, such as global context or detailed spatial arrangement. Given their high resolution, WSIs cannot be processed as a whole and are broken down into smaller pieces called tiles. Then, a prediction at the tile-level is made for each tile in the larger image. As many classification problems require a prediction at a slide-level, there exist common strategies to integrate the tile-level insights into a slide-level prediction. We explore two approaches to tackle this problem, namely a multiple instance learning framework and a representation learning algorithm (the so-called "barcode approach") based on clustering. In this work, we apply both approaches in a single- and multi-scale setting and compare the results in a multi-label histopathology classification task to show the promises and pitfalls of multi-scale analysis. Our work shows a consistent improvement in performance of the multi-scale models over single-scale ones. Using multiple instance learning and the barcode approach we achieved a 0.06 and 0.06 improvement in F1 score, respectively, highlighting the importance of combining multiple scales to integrate contextual and detailed information.
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