首页|Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data

Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data

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  • NETL
  • NSTL
  • IEEE
Building deep learning models that can rapidly segment whole slide images (WSIs) using only a handful of training samples remains an open challenge in computational pathology. The difficulty lies in the histological images themselves: many morphological structures within a slide are closely related and very similar in appearance, making it difficult to distinguish between them. However, a skilled pathologist can quickly identify the relevant phenotypes. Through years of training, they have learned to organize visual features into a hierarchical taxonomy (e.g., identifying carcinoma versus healthy tissue, or distinguishing regions within a tumor as cancer cells, the microenvironment, …). Thus, each region is associated with multiple labels representing different tissue types. Pathologists typically deal with this by analyzing the specimen at multiple scales and comparing visual features between different magnifications. Inspired by this multi-scale diagnostic workflow, we introduce the Navigator, a vision model that navigates through WSIs like a domain expert: it searches for the region of interest at a low scale, zooms in gradually, and localizes ever finer microanatomical classes. As a result, the Navigator can detect coarse-grained patterns at lower resolution and fine-grained features at higher resolution. In addition, to deal with sparsely annotated samples, we train the Navigator with a novel semi-supervised framework called S5CL v2. The proposed model improves the F1 score by up to 8% on various datasets including our challenging new TCGA-COAD-30CLS and Erlangen cohorts.

NavigationCancerTumorsTrainingFeature extractionAnnotationsPhenotypesSemisupervised learningPredictive modelsNeural networks

Manuel Tran、Sophia Wagner、Wilko Weichert、Christian Matek、Melanie Boxberg、Tingying Peng

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Roche Diagnostics, Penzberg, Germany|Helmholtz Munich, Oberschleißheim, Germany|School of Computation, Information and Technology, Technical University of Munich, Munich, Germany

Helmholtz Munich, Oberschleißheim, Germany|School of Computation, Information and Technology, Technical University of Munich, Munich, Germany

German Cancer Consortium, Munich, Germany|German Cancer Research Center, Heidelberg, Germany|Institute of General Pathology and Pathological Anatomy, Technical University of Munich, Munich, Germany

Helmholtz Munich, Oberschleißheim, Germany|Pathology Institute, University Hospital Erlangen, Erlangen, Germany

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2025

10.1109/TMI.2025.3532728

IEEE transactions on medical imaging

IEEE transactions on medical imaging

ISSN：
年,卷(期)：2025.44(5)
  • 77