SPIDER-thorax-model
| Property | Value |
|---|---|
| License | CC BY-NC 4.0 (Research Use Only) |
| Author | HistAI |
| Paper | SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models |
| Performance | Accuracy: 96.2%, Precision: 95.8%, F1: 96.0% |
What is SPIDER-thorax-model?
SPIDER-thorax-model is a specialized deep learning model designed for patch-level pathology classification of thoracic tissue samples. It's part of the larger SPIDER dataset initiative, which provides comprehensive multi-organ pathology data with expert annotations. The model processes 1120×1120 pixel patches and can classify 14 different tissue types including alveoli, bronchial cartilage, tumors, and various pathological conditions.
Implementation Details
The model is implemented using the Transformers library and can be easily integrated into existing workflows. It works with high-resolution pathology images, processing them in 1120×1120 pixel patches. The model was trained on an extensive dataset of 78,307 central patches from 411 different slides, with a total of 599,459 patches including context patches.
- Supports 14 distinct tissue classifications
- Trained on expertly annotated patches
- Implements advanced deep learning architecture
- Provides high-accuracy predictions (96.2%)
Core Capabilities
- Classification of thoracic tissue types including normal and pathological conditions
- Detection of various tumor types (non-small cell, small cell, soft)
- Identification of tissue structures like alveoli, bronchial cartilage, and vessels
- Recognition of pathological conditions such as fibrosis and inflammation
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized focus on thoracic pathology and its exceptional accuracy (96.2%). It's trained on a comprehensive, expert-annotated dataset covering 14 different tissue classes, making it particularly valuable for pathological analysis.
Q: What are the recommended use cases?
This model is specifically designed for research applications in digital pathology, particularly for analyzing thoracic tissue samples. It's ideal for automated classification of tissue types, tumor detection, and identification of pathological conditions in research settings. Note that it's licensed for research use only under CC BY-NC 4.0.
