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README.md

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+# Lighter zoo x CT-FM: Through lighter zoo we provide several models pre-trained using the CT-FM vision foundation model for Computed Tomography (CT) scans.
+CT-FM is a large-scale 3D image-based pre-trained model designed for diverse radiological tasks. The model was pre-trained on 148,000 CT scans from the Imaging Data Commons using label-agnostic contrastive learning.
+## Model Details
+The model demonstrates strong capabilities across multiple tasks:
+- Whole-body
+- Heterogenous umor segmentation
+- Head CT triage
+- Medical image retrieval 
+- Semantic understanding of anatomical structures
+Key features:
+- Learns anatomical clustering without explicit labels
+- Identifies similar anatomical structures across different scans
+- Shows robustness in test-retest scenarios
+- Provides interpretable salient regions in its embeddings
+
+## Models Available
+- Feature extractor `ct_fm_feature_extractor` which can be used for several feature-based tasks such as image retrieval, semantic search and outlier detection
+- Fine-tuned whole body segmentation model `whole_body_segmentation` that segments 117 labels from the TotalSegmentator dataset
+-  
+## Installation
+We provide pre-trained as well as fine-tuned models in the `lighter-zoo` package that interfaces with HF to provide easy to use APIs
+
+To install the `lighter-zoo` package, use pip:
+```bash
+pip install lighter-zoo
+```
+
+Inspect specific models to see how you can interact with these