SPIDER-colorectal-model
| Property | Value |
|---|---|
| License | CC BY-NC 4.0 (Research Use Only) |
| Paper | arXiv:2503.02876 |
| Accuracy | 91.4% |
| Dataset Size | 77,182 central patches, 1,039,150 total patches |
What is SPIDER-colorectal-model?
SPIDER-colorectal-model is a sophisticated deep learning model designed specifically for patch-level pathology classification in colorectal tissue analysis. Part of the larger SPIDER dataset initiative, this model represents a significant advancement in automated pathological analysis, capable of distinguishing between 13 different tissue classes with high precision.
Implementation Details
The model operates on 1120×1120 pixel patches and has been trained on an extensive dataset comprising over 1,700 annotated slides. It utilizes the Transformers architecture and can be easily integrated using the Hugging Face framework.
- Supports 13 distinct tissue classifications including adenocarcinoma, adenoma, and various normal tissue types
- Trained on 77,182 central patches and over 1 million total patches
- Achieves 91.4% accuracy with 91.7% precision and 91.5% F1 score
Core Capabilities
- High-grade and low-grade adenocarcinoma classification
- Adenoma differentiation (high and low grade)
- Normal tissue identification (fat, muscle, vessels)
- Pathological feature detection (inflammation, necrosis)
- Specialized polyp classification (hyperplastic, sessile serrated)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its comprehensive training on the SPIDER dataset, which includes expert-annotated labels across multiple tissue types. Its high accuracy and specialized focus on colorectal pathology make it particularly valuable for research applications.
Q: What are the recommended use cases?
The model is specifically designed for research purposes in pathology laboratories and academic institutions. It excels in automated analysis of colorectal tissue samples, supporting pathologists in classification tasks and potentially accelerating the diagnostic process.
