Summary of Pediatric Brain Tumor Classification Using Digital Histopathology and Deep Learning: Evaluation Of Sota Methods on a Multi-center Swedish Cohort, by Iulian Emil Tampu et al.
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
by Iulian Emil Tampu, Per Nyman, Christoforos Spyretos, Ida Blystad, Alia Shamikh, Gabriela Prochazka, Teresita Díaz de Ståhl, Johanna Sandgren, Peter Lundberg, Neda Haj-Hosseini
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents two weakly supervised multiple-instance learning (MIL) approaches to classify pediatric brain tumors in hematoxylin and eosin whole slide images (WSIs) from a multi-center Swedish cohort. The methods use patch-features obtained from state-of-the-art histology-specific foundation models, specifically ResNet50, UNI, and CONCH, to classify tumors into three hierarchical categories: tumor category, family, and type. The study evaluates model generalization by training on data from two centers and testing on four others, with the highest classification performance achieved using UNI features and attention-based MIL (ABMIL) aggregation. The results show fair generalizability of the models on a multi-center national dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors diagnose brain tumors in children and young adults by using computers to analyze images of brain tissue. They developed two ways to train computers to recognize patterns in these images, which can help identify different types of brain tumors. The researchers tested their methods on a large dataset of images from six hospitals in Sweden and found that they were good at diagnosing different types of brain tumors. This could help doctors provide better treatment for children with brain tumors. |
Keywords
» Artificial intelligence » Attention » Classification » Generalization » Supervised
