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README.md

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----
-license: gpl-3.0
----
+# Deep Tissue Detector - DenseNet121
+
+A deep learning model for detecting tissue regions in Whole Slide Images (WSI) of histopathology slides.
+
+## Model Description
+
+This is a DenseNet121-based tissue detector trained to classify image patches into three categories for histopathology analysis. The model is specifically designed to identify tissue-containing regions in H&E (Hematoxylin and Eosin) stained whole slide images, enabling efficient processing of large pathology datasets.
+
+### Model Details
+
+- **Architecture**: DenseNet121
+- **Input Size**: 224x224 RGB images
+- **Number of Classes**: 3
+  - Class 0: Background/Non-tissue
+  - Class 1: Artifact/Low-quality
+  - Class 2: Tissue (high quality)
+- **Parameters**: ~7M
+- **Model Size**: 28MB
+- **Framework**: PyTorch
+- **License**: GPL-3.0
+
+### Intended Use
+
+This model is designed for:
+- Filtering tissue-containing patches from whole slide images
+- Quality control in computational pathology pipelines
+- Preprocessing for downstream cancer analysis tasks
+- Reducing computational burden by identifying regions of interest
+
+**Recommended Usage**: Accept patches where class 2 (tissue) probability ≥ 0.8
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch torchvision huggingface_hub pillow
+```
+
+### Basic Usage
+
+```python
+from tissue_detector import TissueDetector
+from PIL import Image
+
+# Initialize detector (automatically downloads weights from HuggingFace)
+detector = TissueDetector(device='cuda')
+
+# Load and process an image
+image = Image.open('patch.png')
+patch_transformed = detector.transforms(image)
+patch_batch = patch_transformed.unsqueeze(0).to(detector.device)
+
+# Get predictions
+import torch
+with torch.no_grad():
+    prediction = detector.model(patch_batch)
+    probabilities = torch.nn.functional.softmax(prediction, dim=1).cpu().numpy()[0]
+    tissue_class = probabilities.argmax()
+
+# Check if patch contains tissue (class 2 with threshold 0.8)
+is_tissue = tissue_class == 2 and probabilities[2] >= 0.8
+print(f"Tissue detected: {is_tissue} (confidence: {probabilities[2]:.3f})")
+```
+
+### Integration with HoneyBee Framework
+
+```python
+from honeybee.models import TissueDetector
+from honeybee.processors.wsi import SimpleSlide
+
+# Initialize tissue detector
+detector = TissueDetector(device='cuda')
+
+# Load whole slide image
+slide = SimpleSlide(
+    slide_path='path/to/slide.svs',
+    tile_size=512,
+    max_patches=100,
+    tissue_detector=detector
+)
+
+# Extract tissue patches (automatically filtered)
+patches = slide.load_patches_concurrently(target_patch_size=224)
+```
+
+### Manual Download
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download PyTorch weights
+model_path = hf_hub_download(
+    repo_id="Lab-Rasool/tissue-detector",
+    filename="deep-tissue-detector_densenet_state-dict.pt"
+)
+
+# Or download SafeTensors format (recommended)
+model_path = hf_hub_download(
+    repo_id="Lab-Rasool/tissue-detector",
+    filename="model.safetensors"
+)
+```
+
+## Model Performance
+
+The model has been extensively used in cancer classification, survival analysis, and multimodal integration tasks within the HoneyBee framework. It effectively filters:
+
+- **Background regions**: Glass slide backgrounds, whitespace
+- **Artifacts**: Pen marks, dust, blur, fold artifacts
+- **Tissue regions**: High-quality H&E stained tissue for analysis
+
+**Recommended threshold**: 0.8 probability for class 2 provides a good balance between recall and precision for tissue detection.
+
+## Training Data
+
+The model was originally trained as part of the [SliDL](https://github.com/markowetzlab/slidl) project for tissue detection in histopathology whole slide images. Training details include H&E stained tissue sections with annotations for tissue/non-tissue regions.
+
+## Preprocessing
+
+The model expects images preprocessed with standard ImageNet normalization:
+
+```python
+from torchvision import transforms
+
+preprocessing = transforms.Compose([
+    transforms.Resize(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+```
+
+## Limitations and Biases
+
+- **Stain Variation**: Performance may vary with different staining protocols or scanners
+- **Tissue Types**: Primarily validated on H&E stained tissue; may not generalize to other stains
+- **Resolution**: Designed for patches at standard WSI resolution (~0.5 µm/pixel)
+- **Artifacts**: May misclassify unusual artifacts not seen during training
+- **Medical Use**: This model is for research purposes only and not intended for clinical diagnosis
+
+## Applications in Research
+
+This tissue detector has been used in:
+- **Cancer Classification**: TCGA multi-cancer type classification (BRCA, BLCA, KIRC, LIHC, etc.)
+- **Survival Analysis**: Cox proportional hazards and deep survival models
+- **Stain Normalization Studies**: Evaluating impact of Macenko/Reinhard normalization
+- **Multimodal Integration**: Combining pathology with clinical, radiology, and molecular data
+- **Foundation Model Evaluation**: Preprocessing for UNI, UNI2, Virchow2 embeddings
+
+## Citation
+
+If you use this model, please cite the HoneyBee framework and the original SliDL project:
+
+```bibtex
+@software{honeybee2024,
+  title={HoneyBee: A Scalable Modular Framework for Multimodal AI in Oncology},
+  author={Lab-Rasool},
+  year={2024},
+  url={https://github.com/lab-rasool/HoneyBee}
+}
+
+@software{slidl,
+  title={SliDL: A Python library for deep learning on whole-slide images},
+  author={Markowetz Lab},
+  url={https://github.com/markowetzlab/slidl}
+}
+```
+
+## Model Card Authors
+
+Lab-Rasool
+
+## Model Card Contact
+
+For questions or issues, please open an issue on the [HoneyBee GitHub repository](https://github.com/lab-rasool/HoneyBee).
+
+## Additional Resources
+
+- **HoneyBee Framework**: [https://github.com/lab-rasool/HoneyBee](https://github.com/lab-rasool/HoneyBee)
+- **Documentation**: [https://lab-rasool.github.io/HoneyBee](https://lab-rasool.github.io/HoneyBee)
+- **Original Model Source**: [SliDL GitHub](https://github.com/markowetzlab/slidl)
+
+## Version History
+
+- **v1.0** (2024): Initial release with DenseNet121 architecture
+  - PyTorch state dict format
+  - SafeTensors format for improved loading
+  - Integration with HuggingFace Hub

config.json

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+{
+  "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"
+  ]
+}

deep-tissue-detector_densenet_state-dict.pt

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requirements.txt

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+torch>=1.9.0
+torchvision>=0.10.0
+huggingface_hub>=0.16.0
+pillow>=8.0.0
+numpy>=1.19.0