Update README.md

README.md

@@ -1,173 +1,173 @@
 ---
 license: mit
 dataset_info:
-- config_name: classification
-  features:
-  - name: img_path
-    dtype: string
-  - name: bowel
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': injury
-  - name: extravasation
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': injury
-  - name: kidney
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': low
-          '2': high
-  - name: liver
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': low
-          '2': high
-  - name: spleen
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': low
-          '2': high
-  - name: any_injury
-    dtype: bool
-  - name: metadata
-    struct:
-    - name: series_id
-      dtype: int32
-    - name: patient_id
-      dtype: int32
-    - name: incomplete_organ
-      dtype: bool
-    - name: aortic_hu
-      dtype: float32
-    - name: pixel_representation
-      dtype: int32
-    - name: bits_allocated
-      dtype: int32
-    - name: bits_stored
-      dtype: int32
-  splits:
-  - name: train
-    num_bytes: 802231
-    num_examples: 4239
-  - name: test
-    num_bytes: 89326
-    num_examples: 472
-  download_size: 96729254048
-  dataset_size: 891557
-- config_name: classification-with-mask
-  features:
-  - name: img_path
-    dtype: string
-  - name: seg_path
-    dtype: string
-  - name: bowel
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': injury
-  - name: extravasation
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': injury
-  - name: kidney
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': low
-          '2': high
-  - name: liver
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': low
-          '2': high
-  - name: spleen
-    dtype:
-      class_label:
-        names:
-          '0': healthy
-          '1': low
-          '2': high
-  - name: any_injury
-    dtype: bool
-  - name: metadata
-    struct:
-    - name: series_id
-      dtype: int32
-    - name: patient_id
-      dtype: int32
-    - name: incomplete_organ
-      dtype: bool
-    - name: aortic_hu
-      dtype: float32
-    - name: pixel_representation
-      dtype: int32
-    - name: bits_allocated
-      dtype: int32
-    - name: bits_stored
-      dtype: int32
-  splits:
-  - name: train
-    num_bytes: 58138
-    num_examples: 185
-  - name: test
-    num_bytes: 6600
-    num_examples: 21
-  download_size: 4196738529
-  dataset_size: 64738
-- config_name: segmentation
-  features:
-  - name: img_path
-    dtype: string
-  - name: seg_path
-    dtype: string
-  - name: metadata
-    struct:
-    - name: series_id
-      dtype: int32
-    - name: patient_id
-      dtype: int32
-    - name: incomplete_organ
-      dtype: bool
-    - name: aortic_hu
-      dtype: float32
-    - name: pixel_representation
-      dtype: int32
-    - name: bits_allocated
-      dtype: int32
-    - name: bits_stored
-      dtype: int32
-  splits:
-  - name: train
-    num_bytes: 50714
-    num_examples: 185
-  - name: test
-    num_bytes: 5757
-    num_examples: 21
-  download_size: 4196631843
-  dataset_size: 56471
+  - config_name: classification
+    features:
+      - name: img_path
+        dtype: string
+      - name: bowel
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": injury
+      - name: extravasation
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": injury
+      - name: kidney
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": low
+              "2": high
+      - name: liver
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": low
+              "2": high
+      - name: spleen
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": low
+              "2": high
+      - name: any_injury
+        dtype: bool
+      - name: metadata
+        struct:
+          - name: series_id
+            dtype: int32
+          - name: patient_id
+            dtype: int32
+          - name: incomplete_organ
+            dtype: bool
+          - name: aortic_hu
+            dtype: float32
+          - name: pixel_representation
+            dtype: int32
+          - name: bits_allocated
+            dtype: int32
+          - name: bits_stored
+            dtype: int32
+    splits:
+      - name: train
+        num_bytes: 802231
+        num_examples: 4239
+      - name: test
+        num_bytes: 89326
+        num_examples: 472
+    download_size: 96729254048
+    dataset_size: 891557
+  - config_name: classification-with-mask
+    features:
+      - name: img_path
+        dtype: string
+      - name: seg_path
+        dtype: string
+      - name: bowel
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": injury
+      - name: extravasation
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": injury
+      - name: kidney
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": low
+              "2": high
+      - name: liver
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": low
+              "2": high
+      - name: spleen
+        dtype:
+          class_label:
+            names:
+              "0": healthy
+              "1": low
+              "2": high
+      - name: any_injury
+        dtype: bool
+      - name: metadata
+        struct:
+          - name: series_id
+            dtype: int32
+          - name: patient_id
+            dtype: int32
+          - name: incomplete_organ
+            dtype: bool
+          - name: aortic_hu
+            dtype: float32
+          - name: pixel_representation
+            dtype: int32
+          - name: bits_allocated
+            dtype: int32
+          - name: bits_stored
+            dtype: int32
+    splits:
+      - name: train
+        num_bytes: 58138
+        num_examples: 185
+      - name: test
+        num_bytes: 6600
+        num_examples: 21
+    download_size: 4196738529
+    dataset_size: 64738
+  - config_name: segmentation
+    features:
+      - name: img_path
+        dtype: string
+      - name: seg_path
+        dtype: string
+      - name: metadata
+        struct:
+          - name: series_id
+            dtype: int32
+          - name: patient_id
+            dtype: int32
+          - name: incomplete_organ
+            dtype: bool
+          - name: aortic_hu
+            dtype: float32
+          - name: pixel_representation
+            dtype: int32
+          - name: bits_allocated
+            dtype: int32
+          - name: bits_stored
+            dtype: int32
+    splits:
+      - name: train
+        num_bytes: 50714
+        num_examples: 185
+      - name: test
+        num_bytes: 5757
+        num_examples: 21
+    download_size: 4196631843
+    dataset_size: 56471
 task_categories:
-- image-classification
-- image-segmentation
+  - image-classification
+  - image-segmentation
 pretty_name: RSNA 2023 Abdominal Trauma Detection (Preprocessed)
 size_categories:
-- 1K<n<10K
+  - 1K<n<10K
 ---
 
 # Dataset Card for RSNA 2023 Abdominal Trauma Detection (Preprocessed)
@@ -176,19 +176,19 @@ size_categories:
 
 - **Homepage:** [https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection](https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection)
 - **Source:** [https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data)
-  
 
 ### Dataset Summary
 
-This dataset is the preprocessed version of the dataset from [RSNA 2023 Abdominal Trauma Detection Kaggle Competition](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data). 
+This dataset is the preprocessed version of the dataset from [RSNA 2023 Abdominal Trauma Detection Kaggle Competition](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data).
 
 It is tailored for segmentation and classification tasks. It contains 3 different configs as described below:
+
 - **classification**:
-  - 4711 instances where each instance includes a CT scan in NIfTI format, target labels (e.g., extravasation, bowel, kidney, liver, spleen, any_injury), and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored)
+  - 4711 instances where each instance includes a CT scan in NIfTI format, target labels, and its relevant metadata.
 - **segmentation**:
-  - 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored)
+  - 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, and its relevant metadata.
 - **classification-with-mask**:
-  - 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, target labels (e.g., extravasation, bowel, kidney, liver, spleen, any_injury), and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored)
+  - 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, target labels, and its relevant metadata.
 
 All CT scans and segmentation masks had already been resampled with voxel spacing (2.0, 2.0, 3.0) and thus its reduced file size.
 
@@ -198,81 +198,78 @@ All CT scans and segmentation masks had already been resampled with voxel spacin
 
 #### Configuration 1: classification
 
-- **Size of downloaded dataset files:** {Size in MB}
-- **Size of the generated dataset:** {Size in MB}
-- **Total amount of disk used:** {Total Size in MB}
+- **Size of downloaded dataset files:** 90.09 GiB
 
 An example of an instance in the 'classification' configuration looks as follows:
+
 ```json
 {
-    "img_path": "path/to/CT_scan.nii",
-    "bowel": "healthy",
-    "extravasation": "injury",
-    "kidney": "low",
-    "liver": "high",
-    "spleen": "low",
-    "any_injury": true,
-    "metadata": {
-        "series_id": 12345,
-        "patient_id": 67890,
-        "incomplete_organ": false,
-        "aortic_hu": 135.8,
-        "pixel_representation": 8,
-        "bits_allocated": 16,
-        "bits_stored": 12
-    }
+  "img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/25899/21872.nii.gz",
+  "bowel": 0,
+  "extravasation": 0,
+  "kidney": 0,
+  "liver": 0,
+  "spleen": 0,
+  "any_injury": false,
+  "metadata": {
+    "series_id": 21872,
+    "patient_id": 25899,
+    "incomplete_organ": false,
+    "aortic_hu": 113.0,
+    "pixel_representation": 0,
+    "bits_allocated": 16,
+    "bits_stored": 12
+  }
 }
 ```
 
 #### Configuration 2: segmentation
 
-- **Size of downloaded dataset files:** {Size in MB}
-- **Size of the generated dataset:** {Size in MB}
-- **Total amount of disk used:** {Total Size in MB}
+- **Size of downloaded dataset files:** 3.91 GiB
 
 An example of an instance in the 'segmentation' configuration looks as follows:
+
 ```json
 {
-    "img_path": "path/to/CT_scan.nii",
-    "seg_path": "path/to/segmentation_mask.nii",
-    "metadata": {
-        "series_id": 12345,
-        "patient_id": 67890,
-        "incomplete_organ": true,
-        "aortic_hu": 120.5,
-        "pixel_representation": 8,
-        "bits_allocated": 16,
-        "bits_stored": 12
-    }
+  "img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz",
+  "seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz",
+  "metadata": {
+    "series_id": 4622,
+    "patient_id": 4791,
+    "incomplete_organ": false,
+    "aortic_hu": 223.0,
+    "pixel_representation": 1,
+    "bits_allocated": 16,
+    "bits_stored": 16
+  }
 }
 ```
 
 #### Configuration 3: classification-with-mask
 
-- **Size of downloaded dataset files:** {Size in MB}
-- **Size of the generated dataset:** {Size in MB}
-- **Total amount of disk used:** {Total Size in MB}
+- **Size of downloaded dataset files:** 3.91 GiB
 
 An example of an instance in the 'classification-with-mask' configuration looks as follows:
+
 ```json
 {
-    "img_path": "path/to/CT_scan.nii",
-    "seg_path": "path/to/segmentation_mask.nii",
-    "bowel": "healthy",
-    "extravasation": "injury",
-    "kidney": "low",
-    "liver": "high",
-    "spleen": "low",
-    "any_injury": true,
-    "metadata": {
-        "series_id": 12345,
-        "patient_id": 67890,
-        "incomplete_organ": true,
-        "aortic_hu": 120.5,
-        "pixel_representation": 8,
-        "bits_allocated": 16,
-        "bits_stored": 12
-    }
+  "img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz",
+  "seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz",
+  "bowel": 0,
+  "extravasation": 0,
+  "kidney": 0,
+  "liver": 1,
+  "spleen": 1,
+  "any_injury": true,
+  "metadata": {
+    "series_id": 4622,
+    "patient_id": 4791,
+    "incomplete_organ": false,
+    "aortic_hu": 223.0,
+    "pixel_representation": 1,
+    "bits_allocated": 16,
+    "bits_stored": 16
+  }
 }
 ```
 
@@ -295,16 +292,18 @@ The data fields for all configurations are as follows:
 
 ### Data Splits
 
-Default split: 
+Default split:
+
 - 0.9:0.1 with random_state = 42
 
-| Configuration Name | Train (n_samples) | Test (n_samples) |
-|--------------------|------:|-----:|
-| classification | 4239 | 472 |
-| segmentation | 185 | 21 |
-| classification-with-mask | 185 | 21 |
+| Configuration Name       | Train (n_samples) | Test (n_samples) |
+| ------------------------ | ----------------: | ---------------: |
+| classification           |              4239 |              472 |
+| segmentation             |               185 |               21 |
+| classification-with-mask |               185 |               21 |
 
 Modify the split proportion:
+
 ```python
 rsna_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", test_size=0.05, random_state=42)
 ```
@@ -312,15 +311,18 @@ rsna_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classific
 ### Citation Information
 
 - Preprocessed dataset:
-```  
+
+```
 @InProceedings{huggingface:dataset,
 title = {RSNA 2023 Abdominal Trauma Detection Dataset (Preprocessed)},
 author={Hong Jia Herng},
 year={2023}
 }
 ```
+
 - Original dataset:
-```  
+
+```
 @misc{rsna-2023-abdominal-trauma-detection,
     author = {Errol Colak, Hui-Ming Lin, Robyn Ball, Melissa Davis, Adam Flanders, Sabeena Jalal, Kirti Magudia, Brett Marinelli, Savvas Nicolaou, Luciano Prevedello, Jeff Rudie, George Shih, Maryam Vazirabad, John Mongan},
     title = {RSNA 2023 Abdominal Trauma Detection},
@@ -328,4 +330,4 @@ year={2023}
     year = {2023},
     url = {https://kaggle.com/competitions/rsna-2023-abdominal-trauma-detection}
 }
-```
+```