Add comprehensive sex and tissue site parameter support

This commit enhances the Aeon inference pipeline with complete support for sex and tissue site parameters across the entire application:

- Add sex and tissue site encoding functions with CSV-based mapping
- Update Aeon model to load correct target mappings from metadata
- Add CLI arguments for sex and tissue site in gradio_app.py
- Add validation for sex and tissue site in settings
- Update dependencies: add lightning, seaborn, and statsmodels
- Fix tissue site map file path references

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

pyproject.toml

@@ -10,11 +10,14 @@ readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
   "gradio>=5.49.0",
+  "lightning>=2.6.0",
   "loguru>=0.7.3",
   "memory-profiler>=0.61.0",
   "mussel[torch-gpu]",
   "paladin",
+  "seaborn>=0.13.2",
   "spaces>=0.30.0",
+  "statsmodels>=0.14.6",
 ]
 
 [project.scripts]

src/mosaic/analysis.py

@@ -181,7 +181,7 @@ def _run_aeon_inference(features, site_type, num_workers, sex=None, tissue_site_
     logger.info("Running Aeon for cancer subtype inference")
     aeon_results, _ = run_aeon(
         features=features,
-        model_path="data/aeon_model.pkl",
+        model_path="data/aeon_model_new.pkl",
         metastatic=(site_type == "Metastatic"),
         batch_size=8,
         num_workers=num_workers,
@@ -520,17 +520,15 @@ def analyze_slide(
             logger.warning(traceback.format_exc())
     
     # Convert sex and tissue_site to indices for Aeon model
+    from mosaic.inference.data import encode_sex, encode_tissue_site
+
     sex_idx = None
-    if sex and sex != "Unknown":
-        sex_idx = 0 if sex == "Male" else 1
-    
+    if sex:
+        sex_idx = encode_sex(sex)
+
     tissue_site_idx = None
-    if tissue_site and tissue_site != "Unknown":
-        from mosaic.inference.data import get_tissue_site_map
-        tissue_site_map = get_tissue_site_map()
-        tissue_site_idx = tissue_site_map.get(tissue_site)
-        if tissue_site_idx is None:
-            logger.warning(f"Unknown tissue site: {tissue_site}")
+    if tissue_site:
+        tissue_site_idx = encode_tissue_site(tissue_site)
     
     if is_logged_in:
         logger.info("Using 300s GPU allocation (logged-in user)")

src/mosaic/gradio_app.py

@@ -21,6 +21,7 @@ from mosaic.ui.utils import (
     validate_settings,
     IHC_SUBTYPES,
     SETTINGS_COLUMNS,
+    SEX_OPTIONS,
 )
 from mosaic.analysis import analyze_slide
 
@@ -43,10 +44,10 @@ def download_and_process_models():
         "data/paladin_model_map.csv",
     )
     cancer_subtypes = model_map["cancer_subtype"].unique().tolist()
-    cancer_subtype_name_map = {
+    cancer_subtype_name_map = {"Unknown": "UNK"}
+    cancer_subtype_name_map.update({
         f"{get_oncotree_code_name(code)} ({code})": code for code in cancer_subtypes
-    }
-    cancer_subtype_name_map["Unknown"] = "UNK"
+    })
     reversed_cancer_subtype_name_map = {
         value: key for key, value in cancer_subtype_name_map.items()
     }
@@ -99,6 +100,19 @@ def main():
         default="Primary",
         help="Site type of the slide (for single slide processing)",
     )
+    parser.add_argument(
+        "--sex",
+        type=str,
+        choices=SEX_OPTIONS,
+        default="Unknown",
+        help="Sex of the patient (for single slide processing)",
+    )
+    parser.add_argument(
+        "--tissue-site",
+        type=str,
+        default="Unknown",
+        help="Tissue site of the slide (for single slide processing)",
+    )
     parser.add_argument(
         "--cancer-subtype",
         type=str,
@@ -144,6 +158,8 @@ def main():
                 [
                     args.slide_path,
                     args.site_type,
+                    args.sex,
+                    args.tissue_site,
                     args.cancer_subtype,
                     args.ihc_subtype,
                     args.segmentation_config,
@@ -156,6 +172,8 @@ def main():
             args.slide_path,
             args.segmentation_config,
             args.site_type,
+            args.sex,
+            args.tissue_site,
             args.cancer_subtype,
             cancer_subtype_name_map,
             args.ihc_subtype,
@@ -191,6 +209,8 @@ def main():
             slide_path = row["Slide"]
             seg_config = row["Segmentation Config"]
             site_type = row["Site Type"]
+            sex = row.get("Sex", "Unknown")
+            tissue_site = row.get("Tissue Site", "Unknown")
             cancer_subtype = row["Cancer Subtype"]
             ihc_subtype = row.get("IHC Subtype", "")
             logger.info(
@@ -200,6 +220,8 @@ def main():
                 slide_path,
                 seg_config,
                 site_type,
+                sex,
+                tissue_site,
                 cancer_subtype,
                 cancer_subtype_name_map,
                 ihc_subtype,

src/mosaic/inference/aeon.py

@@ -18,6 +18,8 @@ from mosaic.inference.data import (
     TileFeatureTensorDataset,
     INT_TO_CANCER_TYPE_MAP,
     CANCER_TYPE_TO_INT_MAP,
+    encode_sex,
+    encode_tissue_site,
 )
 
 from loguru import logger
@@ -57,7 +59,7 @@ def run(
     sex=None, tissue_site_idx=None
 ):
     """Run Aeon model inference for cancer subtype prediction.
-    
+
     Args:
         features: NumPy array of tile features extracted from the WSI
         model_path: Path to the pickled Aeon model file
@@ -67,7 +69,7 @@ def run(
         use_cpu: Force CPU usage instead of GPU
         sex: Patient sex (0=Male, 1=Female), optional
         tissue_site_idx: Tissue site index (0-56), optional
-        
+
     Returns:
         tuple: (results_df, part_embedding)
             - results_df: DataFrame with cancer subtypes and confidence scores
@@ -82,6 +84,23 @@ def run(
     model.to(device)
     model.eval()
 
+    # Load the correct mapping from metadata for this model
+    from pathlib import Path
+    import json
+
+    metadata_dir = Path("data/metadata")
+    with open(metadata_dir / "target_dict.tsv") as f:
+        target_dict_str = f.read().strip().replace("'", '"')
+        target_dict = json.loads(target_dict_str)
+
+    histologies = target_dict['histologies']
+    INT_TO_CANCER_TYPE_MAP_LOCAL = {i: histology for i, histology in enumerate(histologies)}
+    CANCER_TYPE_TO_INT_MAP_LOCAL = {v: k for k, v in INT_TO_CANCER_TYPE_MAP_LOCAL.items()}
+
+    # Calculate col_indices_to_drop using local mapping
+    cancer_types_to_drop = ["UDMN", "ADNOS", "CUP", "CUPNOS", "NOT"]
+    col_indices_to_drop_local = [CANCER_TYPE_TO_INT_MAP_LOCAL[x] for x in cancer_types_to_drop if x in CANCER_TYPE_TO_INT_MAP_LOCAL]
+
     site_type = SiteType.METASTASIS if metastatic else SiteType.PRIMARY
 
     # For UI, InferenceDataset will just be a single slide.  Sample id is not relevant.
@@ -105,14 +124,14 @@ def run(
         if "TISSUE_SITE" in batch:
             batch["TISSUE_SITE"] = batch["TISSUE_SITE"].to(device)
         y = model(batch)
-        y["logits"][:, col_indices_to_drop] = -1e6
+        y["logits"][:, col_indices_to_drop_local] = -1e6
 
         batch_size = y["logits"].shape[0]
         assert batch_size == 1
 
         softmax = torch.nn.functional.softmax(y["logits"][0], dim=0)
         argmax = torch.argmax(softmax, dim=0)
-        class_assignment = INT_TO_CANCER_TYPE_MAP[argmax.item()]
+        class_assignment = INT_TO_CANCER_TYPE_MAP_LOCAL[argmax.item()]
         max_confidence = softmax[argmax].item()
         mean_confidence = torch.mean(softmax).item()
 
@@ -123,7 +142,7 @@ def run(
 
         part_embedding = y["whole_part_representation"][0].cpu()
 
-        for cancer_subtype, j in sorted(CANCER_TYPE_TO_INT_MAP.items()):
+        for cancer_subtype, j in sorted(CANCER_TYPE_TO_INT_MAP_LOCAL.items()):
             confidence = softmax[j].item()
             results.append((cancer_subtype, confidence))
         results.sort(key=lambda row: row[1], reverse=True)
@@ -162,6 +181,19 @@ def parse_args():
     parser.add_argument(
         "--metastatic", action="store_true", help="Tissue is from a metastatic site"
     )
+    parser.add_argument(
+        "--sex",
+        type=str,
+        choices=["Male", "Female", "Unknown"],
+        default=None,
+        help="Patient sex (Male or Female)",
+    )
+    parser.add_argument(
+        "--tissue-site",
+        type=str,
+        default=None,
+        help="Tissue site name",
+    )
     parser.add_argument("--batch-size", type=int, default=BATCH_SIZE, help="Batch size")
     parser.add_argument(
         "--num-workers", type=int, default=NUM_WORKERS, help="Number of workers"
@@ -183,6 +215,17 @@ def main():
 
     features = torch.load(opt.features_path)
 
+    # Encode sex and tissue site if provided
+    sex_encoded = None
+    if opt.sex:
+        sex_encoded = encode_sex(opt.sex)
+        logger.info(f"Using sex: {opt.sex} (encoded as {sex_encoded})")
+
+    tissue_site_idx = None
+    if opt.tissue_site:
+        tissue_site_idx = encode_tissue_site(opt.tissue_site)
+        logger.info(f"Using tissue site: {opt.tissue_site} (encoded as {tissue_site_idx})")
+
     results_df, part_embedding = run(
         features=features,
         model_path=opt.model_path,
@@ -190,6 +233,8 @@ def main():
         batch_size=opt.batch_size,
         num_workers=opt.num_workers,
         use_cpu=opt.use_cpu,
+        sex=sex_encoded,
+        tissue_site_idx=tissue_site_idx,
     )
 
     results_df.to_csv(output_path, index=False)

src/mosaic/inference/data.py

@@ -215,8 +215,8 @@ def get_tissue_site_map():
     if _TISSUE_SITE_MAP is None:
         from pathlib import Path
         import pandas as pd
-        
-        csv_path = Path(__file__).parent.parent.parent / "data" / "tissue_site_original_to_idx.csv"
+
+        csv_path = Path(__file__).parent.parent.parent.parent / "data" / "tissue_site_original_to_idx.csv"
         df = pd.read_csv(csv_path)
         
         _TISSUE_SITE_MAP = {}
@@ -236,16 +236,41 @@ def get_tissue_site_options():
     return sorted(set(site_map.keys()))
 
 
+_SEX_MAP = None
+
+
+def get_sex_map():
+    """Get the sex to index mapping from CSV file.
+
+    Returns:
+        dict: Mapping of sex values to indices (0-2)
+    """
+    global _SEX_MAP
+    if _SEX_MAP is None:
+        from pathlib import Path
+        import pandas as pd
+
+        csv_path = Path(__file__).parent.parent.parent.parent / "data" / "sex_original_to_idx.csv"
+        df = pd.read_csv(csv_path)
+
+        _SEX_MAP = {}
+        for _, row in df.iterrows():
+            _SEX_MAP[row['SEX']] = int(row['idx'])
+
+    return _SEX_MAP
+
+
 def encode_sex(sex):
     """Convert sex to numeric encoding.
-    
+
     Args:
-        sex: "Male" or "Female"
-        
+        sex: "Male", "Female", or "Unknown" (case insensitive)
+
     Returns:
-        int: 0 for Male, 1 for Female
+        int: 0 for Male, 1 for Female, 2 for Unknown
     """
-    return 1 if sex == "Female" else 0
+    sex_map = get_sex_map()
+    return sex_map.get(sex, 2)  # Default to 2 (Unknown) if not found
 
 
 def encode_tissue_site(site_name):

src/mosaic/ui/utils.py

@@ -35,13 +35,13 @@ tissue_site_list = None
 
 def get_tissue_sites():
     """Get the list of tissue sites from the tissue site map file.
-    
+
     Returns:
         List of tissue site names
     """
     global tissue_site_list
     if tissue_site_list is None:
-        current_dir = Path(__file__).parent.parent.parent
+        current_dir = Path(__file__).parent.parent.parent.parent
         tissue_site_map_path = current_dir / "data" / "tissue_site_original_to_idx.csv"
         df = pd.read_csv(tissue_site_map_path)
         # Get unique tissue sites and sort them
@@ -149,6 +149,8 @@ def validate_settings(settings_df, cancer_subtype_name_map, cancer_subtypes, rev
     """
     settings_df.columns = SETTINGS_COLUMNS
     warnings = []
+    tissue_sites = get_tissue_sites()
+
     for idx, row in settings_df.iterrows():
         slide_name = row["Slide"]
         subtype = row["Cancer Subtype"]
@@ -166,6 +168,16 @@ def validate_settings(settings_df, cancer_subtype_name_map, cancer_subtypes, rev
                 f"Slide {slide_name}: Unknown site type. Valid types are: Metastatic, Primary. "
             )
             settings_df.at[idx, "Site Type"] = "Primary"
+        if row["Sex"] not in SEX_OPTIONS:
+            warnings.append(
+                f"Slide {slide_name}: Unknown sex. Valid options are: {', '.join(SEX_OPTIONS)}. "
+            )
+            settings_df.at[idx, "Sex"] = "Unknown"
+        if row["Tissue Site"] not in tissue_sites:
+            warnings.append(
+                f"Slide {slide_name}: Unknown tissue site. Valid tissue sites are: {', '.join(tissue_sites)}. "
+            )
+            settings_df.at[idx, "Tissue Site"] = "Unknown"
         if (
             "Breast" not in settings_df.at[idx, "Cancer Subtype"]
             and row["IHC Subtype"] != ""

uv.lock

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 ]
 
+[[package]]
+name = "lightning"
+version = "2.6.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "fsspec", extra = ["http"] },
+    { name = "lightning-utilities" },
+    { name = "packaging" },
+    { name = "pytorch-lightning" },
+    { name = "pyyaml" },
+    { name = "torch" },
+    { name = "torchmetrics" },
+    { name = "tqdm" },
+    { name = "typing-extensions" },
+]
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+]
+
+[[package]]
+name = "lightning-utilities"
+version = "0.15.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging" },
+    { name = "setuptools" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b8/39/6fc58ca81492db047149b4b8fd385aa1bfb8c28cd7cacb0c7eb0c44d842f/lightning_utilities-0.15.2.tar.gz", hash = "sha256:cdf12f530214a63dacefd713f180d1ecf5d165338101617b4742e8f22c032e24", size = 31090, upload-time = "2025-08-06T13:57:39.242Z" }
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+]
+
 [[package]]
 name = "loguru"
 version = "0.7.3"
@@ -2565,11 +2599,14 @@ version = "0.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "gradio" },
+    { name = "lightning" },
     { name = "loguru" },
     { name = "memory-profiler" },
     { name = "mussel", extra = ["torch-gpu"] },
     { name = "paladin" },
+    { name = "seaborn" },
     { name = "spaces" },
+    { name = "statsmodels" },
 ]
 
 [package.dev-dependencies]
@@ -2584,11 +2621,14 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "gradio", specifier = ">=5.49.0" },
+    { name = "lightning", specifier = ">=2.6.0" },
     { name = "loguru", specifier = ">=0.7.3" },
     { name = "memory-profiler", specifier = ">=0.61.0" },
     { name = "mussel", extras = ["torch-gpu"], git = "https://github.com/pathology-data-mining/Mussel.git?rev=mosaic-dev" },
     { name = "paladin", git = "ssh://git@github.com/pathology-data-mining/paladin.git?rev=dev" },
+    { name = "seaborn", specifier = ">=0.13.2" },
     { name = "spaces", specifier = ">=0.30.0" },
+    { name = "statsmodels", specifier = ">=0.14.6" },
 ]
 
 [package.metadata.requires-dev]
@@ -3199,8 +3239,8 @@ wheels = [
 
 [[package]]
 name = "paladin"
-version = "0.1.dev164+g0aef7cad1"
-source = { git = "ssh://git@github.com/pathology-data-mining/paladin.git?rev=dev#0aef7cad1c2c4b54ea75406e3ed4e61c83591a71" }
+version = "0.0.0"
+source = { git = "ssh://git@github.com/pathology-data-mining/paladin.git?rev=dev#de6dab1a40948285d2e8aad322b9aca91ae669e6" }
 dependencies = [
     { name = "dvc" },
     { name = "nn-template-core" },
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 ]
 
+[[package]]
+name = "patsy"
+version = "1.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+]
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+]
+
 [[package]]
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 version = "11.3.0"
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+[[package]]
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+version = "0.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
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+]
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+]
+
 [[package]]
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+[[package]]
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+version = "0.14.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "patsy" },
+    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
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