| { |
| "model_type": "densenet", |
| "architecture": "densenet121", |
| "num_classes": 3, |
| "input_size": [224, 224], |
| "in_channels": 3, |
| "classifier_input_features": 1024, |
| "framework": "pytorch", |
| "task": "image-classification", |
| "domain": "histopathology", |
| "modality": "whole-slide-imaging", |
| "license": "gpl-3.0", |
| "tags": [ |
| "histopathology", |
| "tissue-detection", |
| "whole-slide-imaging", |
| "pathology", |
| "medical-imaging", |
| "densenet", |
| "image-classification", |
| "computational-pathology", |
| "cancer-research" |
| ], |
| "preprocessing": { |
| "resize": 224, |
| "normalization": { |
| "mean": [0.485, 0.456, 0.406], |
| "std": [0.229, 0.224, 0.225] |
| } |
| }, |
| "class_labels": { |
| "0": "background", |
| "1": "artifact", |
| "2": "tissue" |
| }, |
| "recommended_threshold": { |
| "class": 2, |
| "probability": 0.8, |
| "description": "Accept patches where class 2 (tissue) probability >= 0.8" |
| }, |
| "version": "1.0.0", |
| "release_date": "2024", |
| "authors": [ |
| "Lab-Rasool", |
| "Markowetz Lab (original training)" |
| ], |
| "huggingface_repo": "Lab-Rasool/tissue-detector", |
| "related_frameworks": [ |
| "HoneyBee" |
| ] |
| } |
|
|
