TY  - JOUR
AU  - Tran, Manuel
AU  - Wagner, Sophia
AU  - Weichert, Wilko
AU  - Matek, Christian
AU  - Boxberg, Melanie
AU  - Peng, Tingying
TI  - Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data.
JO  - IEEE transactions on medical imaging
VL  - 44
IS  - 5
SN  - 0278-0062
CY  - New York, NY
PB  - Institute of Electrical and Electronics Engineers,
M1  - DKFZ-2025-00984
SP  - 2002 - 2015
PY  - 2025
AB  - 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
KW  - Humans
KW  - Deep Learning
KW  - Image Interpretation, Computer-Assisted: methods
KW  - Algorithms
LB  - PUB:(DE-HGF)16
C6  - pmid:40031287
DO  - DOI:10.1109/TMI.2025.3532728
UR  - https://inrepo02.dkfz.de/record/301312
ER  -