matulichpt
/

radlit-crossencoder

@@ -1,393 +1,387 @@
----
-license: apache-2.0
-language:
-- en
-tags:
-- cross-encoder
-- reranker
-- radiology
-- medical
-- retrieval
-- sentence-similarity
-- healthcare
-- clinical
-base_model: cross-encoder/ms-marco-MiniLM-L-12-v2
-pipeline_tag: text-classification
-library_name: sentence-transformers
-datasets:
-- radiology-education-corpus
-metrics:
-- mrr
-- ndcg
-model-index:
-- name: RadLITE-Reranker
-  results:
-  - task:
-      type: reranking
-      name: Document Reranking
-    dataset:
-      name: RadLIT-9 (Radiology Retrieval Benchmark)
-      type: radiology-retrieval
-    metrics:
-    - type: mrr
-      value: 0.829
-      name: MRR (with bi-encoder)
-    - type: mrr
-      value: 0.533
-      name: MRR on ABR Core Exam (Chest)
----
-# RadLITE-Reranker
-**Radiology Late Interaction Transformer Enhanced - Cross-Encoder Reranker**
-A domain-specialized cross-encoder for reranking radiology search results. This model takes a query-document pair and predicts a relevance score, providing more accurate ranking than bi-encoder similarity alone.
-> **Recommended:** Use this reranker together with [RadLITE-Encoder](https://huggingface.co/matulichpt/radlit-biencoder) in a two-stage pipeline for optimal performance. The bi-encoder handles fast retrieval over large corpora, then this cross-encoder reranks the top candidates for precision. This combination achieves **MRR 0.829** on radiology retrieval benchmarks.
-## Model Description
-| Property | Value |
-|----------|-------|
-| **Model Type** | Cross-Encoder (Reranker) |
-| **Base Model** | [ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2) |
-| **Domain** | Radiology / Medical Imaging |
-| **Hidden Size** | 384 |
-| **Max Sequence Length** | 512 tokens |
-| **Output** | Single relevance score |
-| **License** | Apache 2.0 |
-### Why Use a Reranker?
-Bi-encoders (like RadLITE-Encoder) are fast but encode query and document independently. Cross-encoders process them together, capturing fine-grained interactions:
-| Approach | Speed | Accuracy | Use Case |
-|----------|-------|----------|----------|
-| Bi-Encoder | Fast (1000s docs/sec) | Good | First-stage retrieval |
-| Cross-Encoder | Slow (10s docs/sec) | Excellent | Reranking top candidates |
-**Two-stage pipeline**: Use bi-encoder to get top 50-100 candidates, then rerank with cross-encoder for best results.
-## Performance
-### Impact on RadLIT-9 Benchmark
-| Configuration | MRR | Improvement |
-|---------------|-----|-------------|
-| Bi-Encoder only | 0.78 | baseline |
-| **Bi-Encoder + Reranker** | **0.829** | **+6.3%** |
-### ABR Core Exam (Board-Style Questions)
-Comparing two-stage pipeline (bi-encoder + reranker) vs bi-encoder alone:
-| Dataset | Two-Stage MRR | Bi-Encoder Only | Improvement |
-|---------|---------------|-----------------|-------------|
-| Core Exam Chest | 0.533 | 0.409 | +30.3% |
-| Core Exam Combined | 0.466 | 0.381 | +22.5% |
-The reranker provides significant gains on complex, multi-part queries typical of board exam questions.
-### Published Benchmark Results
-From [Matulich & Mason, 2026](https://huggingface.co/matulichpt/radlit-biencoder):
-| Benchmark | RadLIT Result | Key Finding |
-|-----------|---------------|-------------|
-| NFCorpus nDCG@10 | 0.268 | **17.9x improvement** over RadBERT bi-encoder (0.015) |
-| VQA-RAD MRR | 0.972 | Near-perfect retrieval on radiology Q&A |
-| RadLIT-9 Thoracic | 0.736 nDCG@10 | **Best-in-class** (beat BGE-large, ColBERTv2) |
-| RadLIT-9 Pediatric | 0.625 nDCG@10 | **Best-in-class** (beat BGE-large, ColBERTv2) |
-| Zebra Test | 92% found rate | 2.1x improvement on rare conditions vs ColBERTv2 |
-**Vocabulary Alignment Hypothesis**: Domain training provides measurable advantage when queries use radiology-specific terminology that aligns with the training domain.
-## Quick Start
-### Installation
-```bash
-pip install sentence-transformers>=2.2.0
-```
-### Basic Usage
-```python
-from sentence_transformers import CrossEncoder
-# Load the reranker
-reranker = CrossEncoder("matulichpt/radlit-crossencoder", max_length=512)
-# Query and candidate documents
-query = "What are the imaging features of hepatocellular carcinoma?"
-documents = [
-    "HCC typically shows arterial enhancement with portal venous washout on CT.",
-    "Fatty liver disease presents as decreased attenuation on non-contrast CT.",
-    "Hepatic hemangiomas show peripheral nodular enhancement.",
-]
-# Create query-document pairs
-pairs = [[query, doc] for doc in documents]
-# Get relevance scores
-scores = reranker.predict(pairs)
-# Apply temperature calibration (RECOMMENDED)
-calibrated_scores = scores / 1.5
-print("Scores:", calibrated_scores)
-# Document about HCC will have highest score
-```
-### Temperature Calibration
-**Important**: This model outputs scores with high variance. Apply temperature scaling for better fusion with other signals:
-```python
-# Raw scores might be: [4.2, -1.5, 0.8]
-# After calibration:   [2.8, -1.0, 0.53]
-TEMPERATURE = 1.5  # Recommended value
-def calibrated_predict(reranker, pairs):
-    raw_scores = reranker.predict(pairs)
-    return raw_scores / TEMPERATURE
-```
-### Full Two-Stage Search Pipeline
-```python
-from sentence_transformers import SentenceTransformer, CrossEncoder
-import numpy as np
-class RadLITESearch:
-    def __init__(self, device="cuda"):
-        # Stage 1: Fast bi-encoder
-        self.encoder = SentenceTransformer(
-            "matulichpt/radlit-biencoder",
-            device=device
-        )
-        # Stage 2: Precise reranker
-        self.reranker = CrossEncoder(
-            "matulichpt/radlit-crossencoder",
-            max_length=512,
-            device=device
-        )
-        self.temperature = 1.5
-        self.corpus_embeddings = None
-        self.corpus = None
-    def index_corpus(self, documents: list):
-        """Pre-compute embeddings for your corpus."""
-        self.corpus = documents
-        self.corpus_embeddings = self.encoder.encode(
-            documents,
-            normalize_embeddings=True,
-            show_progress_bar=True,
-            batch_size=32
-        )
-    def search(self, query: str, top_k: int = 10, candidates: int = 50):
-        """Two-stage search: retrieve then rerank."""
-        # Stage 1: Bi-encoder retrieval
-        query_emb = self.encoder.encode(query, normalize_embeddings=True)
-        scores = query_emb @ self.corpus_embeddings.T
-        top_indices = np.argsort(scores)[-candidates:][::-1]
-        # Stage 2: Cross-encoder reranking
-        candidate_docs = [self.corpus[i] for i in top_indices]
-        pairs = [[query, doc] for doc in candidate_docs]
-        rerank_scores = self.reranker.predict(pairs) / self.temperature
-        # Sort by reranked scores
-        sorted_indices = np.argsort(rerank_scores)[::-1]
-        results = []
-        for idx in sorted_indices[:top_k]:
-            results.append({
-                "document": candidate_docs[idx],
-                "corpus_index": int(top_indices[idx]),
-                "score": float(rerank_scores[idx]),
-                "biencoder_score": float(scores[top_indices[idx]])
-            })
-        return results
-# Usage
-searcher = RadLITESearch()
-searcher.index_corpus(your_radiology_documents)
-results = searcher.search("pneumothorax CT findings")
-```
-## Integration with Any Corpus
-### Radiopaedia / Educational Content
-```python
-import json
-# Load your content (e.g., Radiopaedia articles)
-with open("radiopaedia_articles.json") as f:
-    articles = json.load(f)
-corpus = [article["content"] for article in articles]
-# Initialize search
-searcher = RadLITESearch()
-searcher.index_corpus(corpus)
-# Search
-results = searcher.search("classic findings of pulmonary embolism on CTPA")
-for r in results[:5]:
-    print(f"Score: {r['score']:.3f}")
-    print(f"Content: {r['document'][:200]}...")
-    print()
-```
-### Integration with Elasticsearch/OpenSearch
-```python
-from sentence_transformers import CrossEncoder
-reranker = CrossEncoder("matulichpt/radlit-crossencoder", max_length=512)
-def rerank_elasticsearch_results(query: str, es_results: list, top_k: int = 10):
-    """Rerank Elasticsearch BM25 results."""
-    documents = [hit["_source"]["content"] for hit in es_results]
-    pairs = [[query, doc] for doc in documents]
-    scores = reranker.predict(pairs) / 1.5  # Temperature calibration
-    # Combine with ES scores (optional)
-    for i, hit in enumerate(es_results):
-        hit["rerank_score"] = float(scores[i])
-        hit["combined_score"] = 0.3 * hit["_score"] + 0.7 * scores[i]
-    # Sort by combined score
-    reranked = sorted(es_results, key=lambda x: x["combined_score"], reverse=True)
-    return reranked[:top_k]
-```
-## Optimal Fusion Weights
-When combining multiple signals (bi-encoder, cross-encoder, BM25), use these weights:
-```python
-# Optimal weights from grid search on RadLIT-9
-FUSION_WEIGHTS = {
-    "biencoder": 0.5,    # RadLITE-Encoder similarity
-    "crossencoder": 0.2, # RadLITE-Reranker (after temp calibration)
-    "bm25": 0.3          # Lexical matching (if available)
-}
-def fused_score(bienc_score, ce_score, bm25_score=0):
-    return (
-        FUSION_WEIGHTS["biencoder"] * bienc_score +
-        FUSION_WEIGHTS["crossencoder"] * ce_score +
-        FUSION_WEIGHTS["bm25"] * bm25_score
-    )
-```
-## Architecture
-```
-[Query] + [SEP] + [Document]
-           |
-           v
-    [BERT Tokenizer]
-           |
-           v
-    [MiniLM Encoder] (12 layers, 384 hidden)
-           |
-           v
-    [Classification Head]
-           |
-           v
-    Relevance Score (float)
-```
-## Training Details
-- **Base Model**: ms-marco-MiniLM-L-12-v2 (trained on MS MARCO passage ranking)
-- **Fine-tuning**: Radiology query-document relevance pairs
-- **Training Steps**: 5,626
-- **Best Validation Loss**: 0.691
-- **Learning Rate**: 2e-5
-- **Batch Size**: 32
-- **Category Weighting**: Yes (balanced across radiology subspecialties)
-## Best Practices
-### 1. Always Use Temperature Calibration
-Raw cross-encoder scores can be extreme. Temperature scaling (1.5) produces better fusion:
-```python
-calibrated = raw_score / 1.5
-```
-### 2. Limit Candidates for Reranking
-Cross-encoders are slow. Only rerank top 50-100 candidates from bi-encoder:
-```python
-# Good: Rerank top 50
-rerank_candidates = 50
-# Bad: Rerank entire corpus
-rerank_candidates = len(corpus)  # Too slow!
-```
-### 3. Batch Predictions
-```python
-# Efficient: Single batch call
-pairs = [[query, doc] for doc in candidates]
-scores = reranker.predict(pairs, batch_size=32)
-# Inefficient: Individual calls
-scores = [reranker.predict([[query, doc]])[0] for doc in candidates]
-```
-### 4. GPU Acceleration
-```python
-reranker = CrossEncoder(
-    "matulichpt/radlit-crossencoder",
-    max_length=512,
-    device="cuda"  # Use GPU
-)
-```
-## Limitations
-- **English only**: Trained on English radiology text
-- **Speed**: ~10-50 pairs/second (use for reranking, not full corpus)
-- **512 token limit**: Long documents are truncated
-- **Domain-specific**: Optimized for radiology, may underperform on general medical content
-## Citation
-If you use RadLITE in your work, please cite:
-```bibtex
-@article{matulich2026radlit,
-    title = {Late Interaction Retrieval Unlocks Domain Knowledge in Radiology Language Models},
-    author = {Matulich, Patrick and Mason, Dan},
-    year = {2026},
-    journal = {Radiology: Artificial Intelligence},
-    note = {17.9x improvement over RadBERT; best-in-class on Thoracic/Pediatric subspecialties},
-    url = {https://huggingface.co/matulichpt/radlit-biencoder}
-}
-```
-## Related Models
-- [RadLITE-Encoder](https://huggingface.co/matulichpt/radlit-biencoder) - Bi-encoder for first-stage retrieval
-- [RadBERT-RoBERTa-4m](https://huggingface.co/zzxslp/RadBERT-RoBERTa-4m) - Base radiology language model
-## License
-Apache 2.0 - Free for commercial and research use.

+---
+license: apache-2.0
+language:
+- en
+tags:
+- cross-encoder
+- reranker
+- retrieval
+- sentence-similarity
+base_model: cross-encoder/ms-marco-MiniLM-L-12-v2
+pipeline_tag: text-classification
+library_name: sentence-transformers
+metrics:
+- mrr
+- ndcg
+model-index:
+- name: RadLITE-Reranker
+  results:
+  - task:
+      type: reranking
+      name: Document Reranking
+    dataset:
+      name: RadLIT-9 (Radiology Retrieval Benchmark)
+      type: radiology-retrieval
+    metrics:
+    - type: mrr
+      value: 0.829
+      name: MRR (with bi-encoder)
+    - type: mrr
+      value: 0.533
+      name: MRR on ABR Core Exam (Chest)
+---
+# RadLITE-Reranker
+**Radiology Late Interaction Transformer Enhanced - Cross-Encoder Reranker**
+A domain-specialized cross-encoder for reranking radiology search results. This model takes a query-document pair and predicts a relevance score, providing more accurate ranking than bi-encoder similarity alone.
+> **Recommended:** Use this reranker together with [RadLITE-Encoder](https://huggingface.co/matulichpt/radlit-biencoder) in a two-stage pipeline for optimal performance. The bi-encoder handles fast retrieval over large corpora, then this cross-encoder reranks the top candidates for precision. This combination achieves **MRR 0.829** on radiology retrieval benchmarks.
+## Model Description
+| Property | Value |
+|----------|-------|
+| **Model Type** | Cross-Encoder (Reranker) |
+| **Base Model** | [ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2) |
+| **Domain** | Radiology / Medical Imaging |
+| **Hidden Size** | 384 |
+| **Max Sequence Length** | 512 tokens |
+| **Output** | Single relevance score |
+| **License** | Apache 2.0 |
+### Why Use a Reranker?
+Bi-encoders (like RadLITE-Encoder) are fast but encode query and document independently. Cross-encoders process them together, capturing fine-grained interactions:
+| Approach | Speed | Accuracy | Use Case |
+|----------|-------|----------|----------|
+| Bi-Encoder | Fast (1000s docs/sec) | Good | First-stage retrieval |
+| Cross-Encoder | Slow (10s docs/sec) | Excellent | Reranking top candidates |
+**Two-stage pipeline**: Use bi-encoder to get top 50-100 candidates, then rerank with cross-encoder for best results.
+## Performance
+### Impact on RadLIT-9 Benchmark
+| Configuration | MRR | Improvement |
+|---------------|-----|-------------|
+| Bi-Encoder only | 0.78 | baseline |
+| **Bi-Encoder + Reranker** | **0.829** | **+6.3%** |
+### ABR Core Exam (Board-Style Questions)
+Comparing two-stage pipeline (bi-encoder + reranker) vs bi-encoder alone:
+| Dataset | Two-Stage MRR | Bi-Encoder Only | Improvement |
+|---------|---------------|-----------------|-------------|
+| Core Exam Chest | 0.533 | 0.409 | +30.3% |
+| Core Exam Combined | 0.466 | 0.381 | +22.5% |
+The reranker provides significant gains on complex, multi-part queries typical of board exam questions.
+### Published Benchmark Results
+From [Matulich & Mason, 2026](https://huggingface.co/matulichpt/radlit-biencoder):
+| Benchmark | RadLIT Result | Key Finding |
+|-----------|---------------|-------------|
+| NFCorpus nDCG@10 | 0.268 | **17.9x improvement** over RadBERT bi-encoder (0.015) |
+| VQA-RAD MRR | 0.972 | Near-perfect retrieval on radiology Q&A |
+| RadLIT-9 Thoracic | 0.736 nDCG@10 | **Best-in-class** (beat BGE-large, ColBERTv2) |
+| RadLIT-9 Pediatric | 0.625 nDCG@10 | **Best-in-class** (beat BGE-large, ColBERTv2) |
+| Zebra Test | 92% found rate | 2.1x improvement on rare conditions vs ColBERTv2 |
+**Vocabulary Alignment Hypothesis**: Domain training provides measurable advantage when queries use radiology-specific terminology that aligns with the training domain.
+## Quick Start
+### Installation
+```bash
+pip install sentence-transformers>=2.2.0
+```
+### Basic Usage
+```python
+from sentence_transformers import CrossEncoder
+# Load the reranker
+reranker = CrossEncoder("matulichpt/radlit-crossencoder", max_length=512)
+# Query and candidate documents
+query = "What are the imaging features of hepatocellular carcinoma?"
+documents = [
+    "HCC typically shows arterial enhancement with portal venous washout on CT.",
+    "Fatty liver disease presents as decreased attenuation on non-contrast CT.",
+    "Hepatic hemangiomas show peripheral nodular enhancement.",
+]
+# Create query-document pairs
+pairs = [[query, doc] for doc in documents]
+# Get relevance scores
+scores = reranker.predict(pairs)
+# Apply temperature calibration (RECOMMENDED)
+calibrated_scores = scores / 1.5
+print("Scores:", calibrated_scores)
+# Document about HCC will have highest score
+```
+### Temperature Calibration
+**Important**: This model outputs scores with high variance. Apply temperature scaling for better fusion with other signals:
+```python
+# Raw scores might be: [4.2, -1.5, 0.8]
+# After calibration:   [2.8, -1.0, 0.53]
+TEMPERATURE = 1.5  # Recommended value
+def calibrated_predict(reranker, pairs):
+    raw_scores = reranker.predict(pairs)
+    return raw_scores / TEMPERATURE
+```
+### Full Two-Stage Search Pipeline
+```python
+from sentence_transformers import SentenceTransformer, CrossEncoder
+import numpy as np
+class RadLITESearch:
+    def __init__(self, device="cuda"):
+        # Stage 1: Fast bi-encoder
+        self.encoder = SentenceTransformer(
+            "matulichpt/radlit-biencoder",
+            device=device
+        )
+        # Stage 2: Precise reranker
+        self.reranker = CrossEncoder(
+            "matulichpt/radlit-crossencoder",
+            max_length=512,
+            device=device
+        )
+        self.temperature = 1.5
+        self.corpus_embeddings = None
+        self.corpus = None
+    def index_corpus(self, documents: list):
+        """Pre-compute embeddings for your corpus."""
+        self.corpus = documents
+        self.corpus_embeddings = self.encoder.encode(
+            documents,
+            normalize_embeddings=True,
+            show_progress_bar=True,
+            batch_size=32
+        )
+    def search(self, query: str, top_k: int = 10, candidates: int = 50):
+        """Two-stage search: retrieve then rerank."""
+        # Stage 1: Bi-encoder retrieval
+        query_emb = self.encoder.encode(query, normalize_embeddings=True)
+        scores = query_emb @ self.corpus_embeddings.T
+        top_indices = np.argsort(scores)[-candidates:][::-1]
+        # Stage 2: Cross-encoder reranking
+        candidate_docs = [self.corpus[i] for i in top_indices]
+        pairs = [[query, doc] for doc in candidate_docs]
+        rerank_scores = self.reranker.predict(pairs) / self.temperature
+        # Sort by reranked scores
+        sorted_indices = np.argsort(rerank_scores)[::-1]
+        results = []
+        for idx in sorted_indices[:top_k]:
+            results.append({
+                "document": candidate_docs[idx],
+                "corpus_index": int(top_indices[idx]),
+                "score": float(rerank_scores[idx]),
+                "biencoder_score": float(scores[top_indices[idx]])
+            })
+        return results
+# Usage
+searcher = RadLITESearch()
+searcher.index_corpus(your_radiology_documents)
+results = searcher.search("pneumothorax CT findings")
+```
+## Integration with Any Corpus
+### Radiopaedia / Educational Content
+```python
+import json
+# Load your content (e.g., Radiopaedia articles)
+with open("radiopaedia_articles.json") as f:
+    articles = json.load(f)
+corpus = [article["content"] for article in articles]
+# Initialize search
+searcher = RadLITESearch()
+searcher.index_corpus(corpus)
+# Search
+results = searcher.search("classic findings of pulmonary embolism on CTPA")
+for r in results[:5]:
+    print(f"Score: {r['score']:.3f}")
+    print(f"Content: {r['document'][:200]}...")
+    print()
+```
+### Integration with Elasticsearch/OpenSearch
+```python
+from sentence_transformers import CrossEncoder
+reranker = CrossEncoder("matulichpt/radlit-crossencoder", max_length=512)
+def rerank_elasticsearch_results(query: str, es_results: list, top_k: int = 10):
+    """Rerank Elasticsearch BM25 results."""
+    documents = [hit["_source"]["content"] for hit in es_results]
+    pairs = [[query, doc] for doc in documents]
+    scores = reranker.predict(pairs) / 1.5  # Temperature calibration
+    # Combine with ES scores (optional)
+    for i, hit in enumerate(es_results):
+        hit["rerank_score"] = float(scores[i])
+        hit["combined_score"] = 0.3 * hit["_score"] + 0.7 * scores[i]
+    # Sort by combined score
+    reranked = sorted(es_results, key=lambda x: x["combined_score"], reverse=True)
+    return reranked[:top_k]
+```
+## Optimal Fusion Weights
+When combining multiple signals (bi-encoder, cross-encoder, BM25), use these weights:
+```python
+# Optimal weights from grid search on RadLIT-9
+FUSION_WEIGHTS = {
+    "biencoder": 0.5,    # RadLITE-Encoder similarity
+    "crossencoder": 0.2, # RadLITE-Reranker (after temp calibration)
+    "bm25": 0.3          # Lexical matching (if available)
+}
+def fused_score(bienc_score, ce_score, bm25_score=0):
+    return (
+        FUSION_WEIGHTS["biencoder"] * bienc_score +
+        FUSION_WEIGHTS["crossencoder"] * ce_score +
+        FUSION_WEIGHTS["bm25"] * bm25_score
+    )
+```
+## Architecture
+```
+[Query] + [SEP] + [Document]
+           |
+           v
+    [BERT Tokenizer]
+           |
+           v
+    [MiniLM Encoder] (12 layers, 384 hidden)
+           |
+           v
+    [Classification Head]
+           |
+           v
+    Relevance Score (float)
+```
+## Training Details
+- **Base Model**: ms-marco-MiniLM-L-12-v2 (trained on MS MARCO passage ranking)
+- **Fine-tuning**: Radiology query-document relevance pairs
+- **Training Steps**: 5,626
+- **Best Validation Loss**: 0.691
+- **Learning Rate**: 2e-5
+- **Batch Size**: 32
+- **Category Weighting**: Yes (balanced across radiology subspecialties)
+## Best Practices
+### 1. Always Use Temperature Calibration
+Raw cross-encoder scores can be extreme. Temperature scaling (1.5) produces better fusion:
+```python
+calibrated = raw_score / 1.5
+```
+### 2. Limit Candidates for Reranking
+Cross-encoders are slow. Only rerank top 50-100 candidates from bi-encoder:
+```python
+# Good: Rerank top 50
+rerank_candidates = 50
+# Bad: Rerank entire corpus
+rerank_candidates = len(corpus)  # Too slow!
+```
+### 3. Batch Predictions
+```python
+# Efficient: Single batch call
+pairs = [[query, doc] for doc in candidates]
+scores = reranker.predict(pairs, batch_size=32)
+# Inefficient: Individual calls
+scores = [reranker.predict([[query, doc]])[0] for doc in candidates]
+```
+### 4. GPU Acceleration
+```python
+reranker = CrossEncoder(
+    "matulichpt/radlit-crossencoder",
+    max_length=512,
+    device="cuda"  # Use GPU
+)
+```
+## Limitations
+- **English only**: Trained on English radiology text
+- **Speed**: ~10-50 pairs/second (use for reranking, not full corpus)
+- **512 token limit**: Long documents are truncated
+- **Domain-specific**: Optimized for radiology, may underperform on general medical content
+## Citation
+If you use RadLITE in your work, please cite:
+```bibtex
+@article{matulich2026radlit,
+    title = {Late Interaction Retrieval Unlocks Domain Knowledge in Radiology Language Models},
+    author = {Matulich, Patrick and Mason, Dan},
+    year = {2026},
+    journal = {Radiology: Artificial Intelligence},
+    note = {17.9x improvement over RadBERT; best-in-class on Thoracic/Pediatric subspecialties},
+    url = {https://huggingface.co/matulichpt/radlit-biencoder}
+}
+```
+## Related Models
+- [RadLITE-Encoder](https://huggingface.co/matulichpt/radlit-biencoder) - Bi-encoder for first-stage retrieval
+- [RadBERT-RoBERTa-4m](https://huggingface.co/zzxslp/RadBERT-RoBERTa-4m) - Base radiology language model
+## License
+Apache 2.0 - Free for commercial and research use.