Add image-to-text task category

.gitattributes

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Nova.py

@@ -0,0 +1,92 @@
+import os
+import pandas as pd
+from datasets import GeneratorBasedBuilder, SplitGenerator, Split, Value, Features, Image, DatasetInfo, Sequence
+
+
+class NovaDataset(GeneratorBasedBuilder):
+    def _info(self):
+        return DatasetInfo(
+            description="NOVA benchmark: anomaly localization and clinical reasoning in brain MRI.",
+            features=Features({
+                "image": Image(),
+                "filename": Value("string"),
+                "caption": Value("string"),
+                "case_id": Value("string"),
+                "scan_id": Value("string"),
+                "title": Value("string"),
+                "publication_date": Value("string"),
+                "clinical_history": Value("string"),
+                "differential_diagnosis": Value("string"),
+                "final_diagnosis": Value("string"),
+                "link": Value("string"),
+                "bbox_gold": Sequence({
+                    "x": Value("float32"),
+                    "y": Value("float32"),
+                    "width": Value("float32"),
+                    "height": Value("float32"),
+                }),                
+                "bbox_raters": Sequence({
+                    "rater": Value("string"),
+                    "x": Value("float32"),
+                    "y": Value("float32"),
+                    "width": Value("float32"),
+                    "height": Value("float32"),
+                }),            }),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract("test/Images.zip")
+        image_files = list(dl_manager.iter_files(archive_path))
+
+        # Download CSVs
+        csvs = {
+            "captions": dl_manager.download("captions.csv"),
+            "bboxes_gold": dl_manager.download("bboxes_gold.csv"),
+            "bboxes_raters": dl_manager.download("bboxes_raters.csv"),
+            "case_metadata": dl_manager.download("case_metadata.csv"),
+        }
+
+        return [
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={"image_files": image_files, "csvs": csvs},
+            )
+        ]
+
+    def _generate_examples(self, image_files, csvs):
+        captions = pd.read_csv(csvs["captions"])
+        gold = pd.read_csv(csvs["bboxes_gold"])
+        raters = pd.read_csv(csvs["bboxes_raters"])
+        meta = pd.read_csv(csvs["case_metadata"])
+
+        captions["case_id"] = captions["filename"].str.extract(r"^(case\d+)_")
+
+        gold_grouped = gold.groupby("filename").apply(
+            lambda df: df[["x", "y", "width", "height"]].astype(float).to_dict(orient="records")
+        ).to_dict()
+
+        raters_grouped = raters.groupby("filename").apply(
+            lambda df: df[["rater", "x", "y", "width", "height"]].astype({"x": float, "y": float, "width": float, "height": float}).to_dict(orient="records")
+        ).to_dict()
+        meta = meta.set_index("case_id").to_dict("index")
+    
+        images_map = {os.path.basename(f): f for f in image_files}
+
+        for idx, row in captions.iterrows():
+            filename = row["filename"]
+            case_id = row["case_id"]
+            record = {
+                "image": images_map.get(filename, None),
+                "filename": filename,
+                "caption": row["caption"],
+                "case_id": case_id,
+                "scan_id": str(row["scan_id"]),
+                "bbox_gold": gold_grouped.get(filename, []),
+                "bbox_raters": raters_grouped.get(filename, []),
+            }
+            if case_id in meta:
+                record.update(meta[case_id])
+            else:
+                record.update({k: "" for k in ["title", "publication_date", "clinical_history", "differential_diagnosis", "final_diagnosis", "link"]})
+            yield idx, record

README.md

@@ -33,7 +33,7 @@ tags:
 
 <table>
   <tr>
-    <td><img src="https://huggingface.co/datasets/c-i-ber/Nova/resolve/main/logo.png" alt="NOVA Logo" width="40"></td>
+    <td><img src="https://huggingface.co/datasets/Ano-2090/Nova/resolve/main/logo.png" alt="NOVA Logo" width="40"></td>
     <td><h1 style="margin:0; padding-left: 10px;">NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI</h1></td>
   </tr>
 </table>
@@ -41,7 +41,7 @@ tags:
 > **An open-world generalization benchmark under clinical distribution shift**
 
 [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC--BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)  
-[Dataset on 🤗 Hugging Face](https://huggingface.co/datasets/c-i-ber/Nova)  
+[Dataset on 🤗 Hugging Face](https://huggingface.co/datasets/Ano-2090/Nova)  
 *For academic, non-commercial use only*
 
 ---
@@ -50,6 +50,11 @@ tags:
 
 If you find this dataset useful in your work, please consider citing it:
 
+<details>
+  <summary>📖 Click to show citation (⚠️ **Double-blind review warning**: If you are a reviewer, please **do not expand** this section if anonymity must be preserved.)</summary>
+
+  
+
   ```bibtex
   @article{bercea2025nova,
     title={NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI},
@@ -60,8 +65,9 @@ If you find this dataset useful in your work, please consider citing it:
   }
   ```
 
+</details>
+
 [Paper](https://huggingface.co/papers/2505.14064)
-[Evaluation Scripts](https://huggingface.co/c-i-ber/Nova_Evaluation)
 
 ---
 
@@ -69,17 +75,17 @@ If you find this dataset useful in your work, please consider citing it:
 
 ```python
 from datasets import load_dataset
-ds = load_dataset("parquet", data_files=f"hf://datasets/c-i-ber/Nova/data/nova-v1.parquet", split="train")
+dataset = load_dataset("Ano-2090/Nova")
 ```
 
 ## 🧪 Try it out in Colab
 
 You can explore the NOVA dataset directly in your browser using the interactive notebook below:
 
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://huggingface.co/datasets/c-i-ber/Nova/blob/main/notebooks/nova_demo.ipynb)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://huggingface.co/datasets/Ano-2090/Nova/blob/main/example.ipynb)
 
 Or open the notebook directly:
-👉 [example.ipynb](https://huggingface.co/datasets/c-i-ber/Nova/blob/main/notebooks/nova-demo.ipynb)
+👉 [example.ipynb](https://huggingface.co/datasets/Ano-2090/Nova/blob/main/example.ipynb)
 
 ---
 ## 💡 Motivation

build_parquet.py

@@ -1,172 +0,0 @@
-#!/usr/bin/env python3
-import os
-import pandas as pd
-import pyarrow as pa
-import pyarrow.parquet as pq
-
-# —— Inputs (adjust if paths differ)
-CAPTIONS_CSV = "captions.csv"
-META_CSV     = "case_metadata.csv"
-GOLD_CSV     = "bboxes_gold.csv"
-RATERS_CSV   = "bboxes_raters.csv"
-IMAGES_DIR   = "images"
-
-OUT_DIR      = "data"
-OUT_PARQUET  = os.path.join(OUT_DIR, "nova-v1.parquet")
-
-os.makedirs(OUT_DIR, exist_ok=True)
-
-# —— Load CSVs
-captions = pd.read_csv(CAPTIONS_CSV)
-meta     = pd.read_csv(META_CSV)
-gold     = pd.read_csv(GOLD_CSV)
-raters   = pd.read_csv(RATERS_CSV)
-
-# —— Normalize key columns as strings
-for df in (captions, meta, gold, raters):
-    if "case_id" in df.columns:
-        df["case_id"] = df["case_id"].astype(str)
-    if "scan_id" in df.columns:
-        df["scan_id"] = df["scan_id"].astype(str)
-    if "filename" in df.columns:
-        df["filename"] = df["filename"].astype(str)
-
-# —— Build the master list of images (from captions)
-# If you prefer, you can take the union of filenames from all CSVs.
-images = captions[["filename", "case_id", "scan_id", "caption"]].copy()
-images["caption"] = images["caption"].fillna("").astype(str)
-
-images.rename(columns={"caption": "caption_text"}, inplace=True)
-
-# Add image_path and split
-images["image_path"] = IMAGES_DIR + "/" + images["filename"]
-images["split"] = "test"
-
-# —— Prepare per-case metadata as a dict so we can merge by case_id
-meta_cols = [
-    "title", "publication_date", "clinical_history",
-    "differential_diagnosis", "final_diagnosis", "link"
-]
-meta = meta[["case_id"] + meta_cols].copy()
-
-# Merge per-image with per-case meta (left join by case_id)
-df = images.merge(meta, on="case_id", how="left")
-
-# —— Prepare gold bboxes
-# Required columns: filename, x, y, width, height
-gold_cols = ["filename", "x", "y", "width", "height"]
-gold_use = gold[["filename", "x", "y", "width", "height"]].copy()
-gold_use["source"] = "gold"
-gold_use["label"] = ""  # no label provided; keep empty string
-
-# —— Prepare rater bboxes
-# Required columns: filename, rater, x, y, width, height
-r_cols = ["filename", "rater", "x", "y", "width", "height"]
-raters_use = raters[r_cols].copy()
-raters_use.rename(columns={"rater": "source"}, inplace=True)
-raters_use["label"] = ""
-
-# —— Stack gold + raters, then group per filename into list[struct]
-all_boxes = pd.concat([gold_use, raters_use], ignore_index=True)
-
-def pack_boxes(group: pd.DataFrame):
-    # Convert rows -> list of dicts with fixed keys
-    return [
-        {
-            "x": float(row["x"]),
-            "y": float(row["y"]),
-            "width": float(row["width"]),
-            "height": float(row["height"]),
-            "source": str(row["source"]),
-            "label": str(row["label"]),
-        }
-        for _, row in group.iterrows()
-    ]
-
-boxes_per_file = (
-    all_boxes
-    .groupby("filename", sort=False, group_keys=False)
-    .apply(pack_boxes)
-    .rename("bboxes")
-    .reset_index()
-)
-
-# Attach bboxes to df by filename; missing -> empty list
-df = df.merge(boxes_per_file, on="filename", how="left")
-df["bboxes"] = df["bboxes"].apply(lambda v: v if isinstance(v, list) else [])
-
-# —— Build a nested Arrow schema for clean typing
-bbox_struct = pa.struct([
-    ("x", pa.float64()),
-    ("y", pa.float64()),
-    ("width", pa.float64()),
-    ("height", pa.float64()),
-    ("source", pa.string()),
-    ("label", pa.string()),
-])
-
-meta_struct = pa.struct([
-    ("title", pa.string()),
-    ("publication_date", pa.string()),  # keep as-is (e.g., 30.03.2022); can normalize later
-    ("clinical_history", pa.string()),
-    ("differential_diagnosis", pa.string()),
-    ("final_diagnosis", pa.string()),
-    ("link", pa.string()),
-])
-
-schema = pa.schema([
-    ("image_path", pa.string()),
-    ("filename", pa.string()),
-    ("split", pa.string()),
-    ("case_id", pa.string()),
-    ("scan_id", pa.string()),
-    ("caption_text", pa.string()),
-    ("bboxes", pa.list_(bbox_struct)),
-    ("meta", meta_struct),
-])
-for col in ["title", "publication_date", "clinical_history", "differential_diagnosis", "final_diagnosis", "link"]:
-    if col in df.columns:
-        df[col] = df[col].fillna("").astype(str)
-# —— Build Arrow arrays column by column
-def to_arrow_array_list_of_struct(list_of_dicts, struct_type):
-    # list_of_dicts: python list of dicts with keys matching struct_type
-    # returns: pa.Array(list<struct>)
-    if not list_of_dicts:
-        # represent empty with empty list
-        return pa.array([], type=pa.list_(struct_type))
-    # we will build a ListArray from a chunk; handle per row later
-    raise RuntimeError("This helper is row-wise; we will build below with pa.array on the full column.")
-
-# Build meta struct column
-meta_dicts = df[meta_cols].to_dict(orient="records")
-meta_array = pa.array(meta_dicts, type=meta_struct)
-
-# Build bboxes list<struct> column
-# Flatten per row using pa.array with explicit type
-bboxes_array = pa.array(df["bboxes"].tolist(), type=pa.list_(bbox_struct))
-
-# Build the rest as Arrow arrays
-table = pa.Table.from_arrays(
-    [
-        pa.array(df["image_path"].tolist(), type=pa.string()),
-        pa.array(df["filename"].tolist(), type=pa.string()),
-        pa.array(df["split"].tolist(), type=pa.string()),
-        pa.array(df["case_id"].tolist(), type=pa.string()),
-        pa.array(df["scan_id"].tolist(), type=pa.string()),
-        pa.array(df["caption_text"].tolist(), type=pa.string()),
-        bboxes_array,
-        meta_array,
-    ],
-    schema=schema
-)
-
-# —— Optional: sanity checks
-# - ensure files referenced actually exist
-missing = [p for p in df["image_path"] if not os.path.exists(p)]
-if missing:
-    print(f"[WARN] {len(missing)} image paths do not exist locally. First few:", missing[:5])
-
-# —— Write Parquet
-pq.write_table(table, OUT_PARQUET)
-print(f"Wrote {OUT_PARQUET} with {table.num_rows} rows.")
-

croissant.json

@@ -1,127 +0,0 @@
-{
-  "@context": {
-    "@language": "en",
-    "@vocab": "https://schema.org/",
-    "cr": "http://mlcommons.org/croissant/",
-    "dct": "http://purl.org/dc/terms/",
-    "recordSet": "cr:recordSet",
-    "field": "cr:field",
-    "fileSet": "cr:fileSet",
-    "fileObject": "cr:fileObject",
-    "dataType": { "@id": "cr:dataType", "@type": "@vocab" },
-    "source": "cr:source",
-    "extract": "cr:extract",
-    "subField": "cr:subField",
-    "repeated": "cr:repeated",
-    "key": "cr:key",
-    "path": "cr:path",
-    "column": "cr:column",
-    "conformsTo": "dct:conformsTo",
-    "citeAs": "cr:citeAs"
-  },
-  "@type": ["Dataset"],                    
-  "name": "NOVA",
-  "description": "Annotated brain MRI cases with radiology captions, gold + multi-rater bounding boxes, and clinical metadata.",
-  "url": "https://huggingface.co/datasets/c-i-ber/Nova",
-  "license": "https://creativecommons.org/licenses/by/4.0/",
-  "version": "1.0.0",
-  "citeAs": "Bercea C. et al., NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI, 2025.",
-  "conformsTo": "http://mlcommons.org/croissant/1.1",
-
-  "distribution": [
-    {
-      "@type": "cr:FileObject",
-      "@id": "local-parquet",
-      "name": "NOVA Parquet",
-      "contentUrl": "https://huggingface.co/datasets/c-i-ber/Nova/resolve/main/data/nova-v1.parquet",
-      "encodingFormat": "application/x-parquet",
-      "sha256": "71880c12b710b8c82100da51d574f13312cc4811fb5b20073ef4f5d98d22a319"
-    }
-  ],
-
-  "recordSet": [
-    {
-      "@type": "cr:RecordSet",
-      "@id": "records",
-      "name": "records",
-      "description": "One row per image.",
-      "key": { "@id": "records/filename" },
-      "field": [
-        {
-          "@type": "cr:Field",
-          "@id": "records/image_path",
-          "dataType": "Text",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "image_path" } }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/filename",
-          "dataType": "Text",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "filename" } }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/split",
-          "dataType": "Text",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "split" } }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/case_id",
-          "dataType": "Text",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "case_id" } }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/scan_id",
-          "dataType": "Text",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "scan_id" } }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/caption_text",
-          "dataType": "Text",
-          "description": "Radiology caption.",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "caption_text" } }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/image",
-          "dataType": "ImageObject",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "image" } }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/bboxes",
-          "dataType": "StructuredValue",
-          "description": "Bounding boxes in pixel coords with source (gold/raters).",
-          "repeated": true,
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "bboxes" } },
-          "subField": [
-            { "@type": "cr:Field", "@id": "records/bboxes/x",      "dataType": "Float" },
-            { "@type": "cr:Field", "@id": "records/bboxes/y",      "dataType": "Float" },
-            { "@type": "cr:Field", "@id": "records/bboxes/width",  "dataType": "Float" },
-            { "@type": "cr:Field", "@id": "records/bboxes/height", "dataType": "Float" },
-            { "@type": "cr:Field", "@id": "records/bboxes/source", "dataType": "Text" },
-            { "@type": "cr:Field", "@id": "records/bboxes/label",  "dataType": "Text" }
-          ]
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "records/meta",
-          "dataType": "StructuredValue",
-          "description": "Per-case metadata (Eurorad).",
-          "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "meta" } },
-          "subField": [
-            { "@type": "cr:Field", "@id": "records/meta/title",                  "dataType": "Text" },
-            { "@type": "cr:Field", "@id": "records/meta/publication_date",       "dataType": "Text" },
-            { "@type": "cr:Field", "@id": "records/meta/clinical_history",       "dataType": "Text" },
-            { "@type": "cr:Field", "@id": "records/meta/differential_diagnosis", "dataType": "Text" },
-            { "@type": "cr:Field", "@id": "records/meta/final_diagnosis",        "dataType": "Text" },
-            { "@type": "cr:Field", "@id": "records/meta/link",                   "dataType": "URL" }
-          ]
-        }
-      ]
-    }
-  ]
-}

data/nova-v1.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:71880c12b710b8c82100da51d574f13312cc4811fb5b20073ef4f5d98d22a319
-size 199088

dataset_description.json

@@ -0,0 +1,433 @@
+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "data": {
+      "@id": "cr:data",
+      "@type": "@json"
+    },
+    "dataType": {
+      "@id": "cr:dataType",
+      "@type": "@vocab"
+    },
+    "dct": "http://purl.org/dc/terms/",
+    "examples": {
+      "@id": "cr:examples",
+      "@type": "@json"
+    },
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "NOVA",
+  "description": "NOVA benchmark: anomaly localization and clinical reasoning in brain MRI.",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "license": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+  "url": "https://huggingface.co/datasets/Ano-2090/Nova",
+  "distribution": [
+    {
+      "@type": "cr:FileObject",
+      "@id": "captions-clean",
+      "name": "captions-clean",
+      "description": "CSV file with NOVA image captions and metadata.",
+      "contentUrl": "captions.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "d696a6259d9a7c8023ab48462521b26b9af6571ca8bc22daf7c3dff13a42ff28"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "bboxes-gold",
+      "name": "bboxes-gold",
+      "description": "CSV with gold-standard bounding boxes annotated by radiologists.",
+      "contentUrl": "bboxes_gold.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "6cb93de17bef13b28913e73b3b2060c660c314cc88805156c486b6657d77dcc2"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "bboxes-raters",
+      "name": "bboxes-raters",
+      "description": "CSV with bounding boxes annotated by independent raters.",
+      "contentUrl": "bboxes_raters.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "56df18b177a77e8a6587babc7220732cfb38629cec56fde8ca05fef76c10d59a"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "case-metadata",
+      "name": "case-metadata",
+      "description": "CSV with clinical case metadata, including diagnosis and history.",
+      "contentUrl": "case_metadata.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "42b34bb6673564de73a921e731de4819a3029bfbf563f2755d45ed98de9760d1"
+    }
+  ],
+  "recordSet": [
+    {
+      "@type": "cr:RecordSet",
+      "@id": "captions",
+      "name": "captions",
+      "description": "Captions and identifiers for NOVA images.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "captions/filename",
+          "name": "filename",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "captions-clean"
+            },
+            "extract": {
+              "column": "filename"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "captions/caption",
+          "name": "caption",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "captions-clean"
+            },
+            "extract": {
+              "column": "caption"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "captions/case_id",
+          "name": "case_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "captions-clean"
+            },
+            "extract": {
+              "column": "case_id"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "captions/scan_id",
+          "name": "scan_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "captions-clean"
+            },
+            "extract": {
+              "column": "scan_id"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "bboxes_gold",
+      "name": "bboxes_gold",
+      "description": "Gold-standard bounding boxes per image.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_gold/filename",
+          "name": "filename",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-gold"
+            },
+            "extract": {
+              "column": "filename"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_gold/x",
+          "name": "x",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-gold"
+            },
+            "extract": {
+              "column": "x"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_gold/y",
+          "name": "y",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-gold"
+            },
+            "extract": {
+              "column": "y"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_gold/width",
+          "name": "width",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-gold"
+            },
+            "extract": {
+              "column": "width"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_gold/height",
+          "name": "height",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-gold"
+            },
+            "extract": {
+              "column": "height"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "bboxes_raters",
+      "name": "bboxes_raters",
+      "description": "Bounding boxes by other human raters.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_raters/filename",
+          "name": "filename",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-raters"
+            },
+            "extract": {
+              "column": "filename"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_raters/rater",
+          "name": "rater",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-raters"
+            },
+            "extract": {
+              "column": "rater"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_raters/x",
+          "name": "x",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-raters"
+            },
+            "extract": {
+              "column": "x"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_raters/y",
+          "name": "y",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-raters"
+            },
+            "extract": {
+              "column": "y"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_raters/width",
+          "name": "width",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-raters"
+            },
+            "extract": {
+              "column": "width"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "bboxes_raters/height",
+          "name": "height",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "bboxes-raters"
+            },
+            "extract": {
+              "column": "height"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "case_metadata",
+      "name": "case_metadata",
+      "description": "Case metadata including diagnosis and clinical history.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "case_metadata/case_id",
+          "name": "case_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "case-metadata"
+            },
+            "extract": {
+              "column": "case_id"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "case_metadata/title",
+          "name": "title",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "case-metadata"
+            },
+            "extract": {
+              "column": "title"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "case_metadata/publication_date",
+          "name": "publication_date",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "case-metadata"
+            },
+            "extract": {
+              "column": "publication_date"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "case_metadata/clinical_history",
+          "name": "clinical_history",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "case-metadata"
+            },
+            "extract": {
+              "column": "clinical_history"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "case_metadata/differential_diagnosis",
+          "name": "differential_diagnosis",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "case-metadata"
+            },
+            "extract": {
+              "column": "differential_diagnosis"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "case_metadata/final_diagnosis",
+          "name": "final_diagnosis",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "case-metadata"
+            },
+            "extract": {
+              "column": "final_diagnosis"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "case_metadata/link",
+          "name": "link",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "case-metadata"
+            },
+            "extract": {
+              "column": "link"
+            }
+          }
+        }
+      ]
+    }
+  ]
+}

example.ipynb

@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🧠 NOVA Benchmark: Extreme Stress-Test for Out-of-Distribution Detection in Brain MRI\n",
+    "\n",
+    "Welcome to the NOVA dataset — a carefully curated, evaluation-only benchmark designed to push the limits of machine learning models in real-world clinical scenarios. With over **900 brain MRI scans**, **281 rare pathologies**, and **rich clinical metadata**, NOVA goes beyond traditional anomaly detection.\n",
+    "\n",
+    "This notebook walks you through how to:\n",
+    "\n",
+    "- Load the NOVA dataset directly from Hugging Face 🤗\n",
+    "- Access images, captions, and diagnostic metadata\n",
+    "- Visualize expert-annotated bounding boxes (gold standard and raters)\n",
+    "- Explore one of the most challenging testbeds for generalization and reasoning under uncertainty\n",
+    "\n",
+    "> ⚠️ This benchmark is intended **only for evaluation**. No training should be performed on NOVA.\n",
+    "\n",
+    "📘 For more details, visit the [dataset page on Hugging Face](https://huggingface.co/datasets/Ano-2090/Nova).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use('agg')\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "from datasets import load_dataset\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = load_dataset(\"Ano-2090/Nova\", trust_remote_code=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Select a random example\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example = random.choice(ds[\"test\"])\n",
+    "image = example[\"image\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create figure and display image\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(8, 8))\n",
+    "ax.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot gold standard bounding boxes (gold)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bbox = example[\"bbox_gold\"]\n",
+    "for x, y, w, h in zip(bbox[\"x\"], bbox[\"y\"], bbox[\"width\"], bbox[\"height\"]):\n",
+    "    rect = patches.Rectangle((x, y), w, h, linewidth=2, edgecolor=\"gold\", facecolor=\"none\")\n",
+    "    ax.add_patch(rect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot rater bounding boxes (turquoise, salmon) with labels\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = ['#40E0D0', '#FA8072']\n",
+    "raters = example[\"bbox_raters\"]\n",
+    "if raters:\n",
+    "    for i in range(len(raters[\"x\"])):\n",
+    "        rater = raters[\"rater\"][i]\n",
+    "        x = raters[\"x\"][i]\n",
+    "        y = raters[\"y\"][i]\n",
+    "        w = raters[\"width\"][i]\n",
+    "        h = raters[\"height\"][i]\n",
+    "        rect = patches.Rectangle((x, y), w, h, linewidth=1.5, edgecolor=colors[i], facecolor=\"none\", linestyle=\"--\")\n",
+    "        ax.add_patch(rect)\n",
+    "        if i == 0:\n",
+    "            ax.text(x, y - 5, rater, color=colors[i], fontsize=8, weight=\"bold\", va=\"bottom\")\n",
+    "        else: \n",
+    "            ax.text(x + w/2, y + h + 15, rater, color=colors[i], fontsize=8, weight=\"bold\", va=\"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize example\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.title(f'{example[\"filename\"]} — {example[\"final_diagnosis\"]}', fontsize=12)\n",
+    "plt.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "display(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print other metadata \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('*-------------------------------------------------------*')\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('caption:', example[\"caption\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('clinical history:', example[\"clinical_history\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('differential diagnosis:', example[\"differential_diagnosis\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('final diagnosis:', example[\"final_diagnosis\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('*-------------------------------------------------------*')"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

images/.DS_Store

@@ -1,3 +0,0 @@
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