Update README: document BraTS nested-by-volume layout

README.md

@@ -52,9 +52,17 @@ MedSeg-7D/
 │   └── split_info.json           CANONICAL patient-level split (seed=42, 80/20)
 │
 ├── BraTS2020/                    (brain MRI FLAIR, 369 volumes → 22677 slices)
-│   ├── images/                   volume_<id>_slice_<s>.png  (FLAIR channel)
-│   ├── masks/                    matching whole-tumor binary mask
+│   ├── images/
+│   │   ├── volume_1/             volume_1_slice_<s>.png  (FLAIR channel, ~50-80 slices/vol)
+│   │   ├── volume_2/
+│   │   └── ... (369 vols)
+│   ├── masks/
+│   │   ├── volume_1/             matching whole-tumor binary mask
+│   │   └── ... (369 vols)
 │   └── split_info.json           CANONICAL volume-level split (seed=42, 295/74)
+│   # NOTE: BraTS slices are nested into per-volume subdirectories because of
+│   # HuggingFace's 10000 files-per-directory limit. Filenames preserve the
+│   # original volume_X_slice_Y.png convention.
 │
 ├── BUSI/                         (breast ultrasound, 780 images)
 │   ├── images/
@@ -215,9 +223,13 @@ info = json.load(open(os.path.join(ROOT, "ACDC", "split_info.json")))
 train_patients = set(info["train_patients"])
 # enumerate slices, check patient ID in filename to assign train/test
 
-# BraTS (volume-level) — same pattern
+# BraTS (volume-level) — slices are nested under per-volume subdirs
 info = json.load(open(os.path.join(ROOT, "BraTS2020", "split_info.json")))
 train_volumes = set(info["train_patients"])  # key name retained from original
+# To enumerate all training slices:
+#   for vol in train_volumes:
+#       for img_path in glob.glob(f"{ROOT}/BraTS2020/images/{vol}/*.png"):
+#           ...
 
 # CVC (video-level — recommended) or image-level (legacy)
 info = json.load(open(os.path.join(ROOT, "CVC-ClinicDB", "video_split_seed42.json")))