Add example nii.gz images and dataset.json for Dataset005 and Dataset007

Dataset005_MU_Glioma_Post/dataset.json

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+{
+    "channel_names": {
+        "0": "t1c",
+        "1": "t1n",
+        "2": "t2f",
+        "3": "t2w"
+    },
+    "labels": {
+        "background": 0,
+        "NCR": 1,
+        "ED": 2,
+        "ET": 3,
+        "NET_RC": 4
+    },
+    "classification_labels": {
+        "primary_diagnosis": {
+            "0": "GBM",
+            "1": "Astrocytoma",
+            "2": "Others"
+        }
+    },
+    "numTraining": 591,
+    "file_ending": ".nii.gz",
+    "overwrite_image_reader_writer": "SimpleITKIO",
+    "license": "CC-BY-NC 4.0",
+    "description": "MU-Glioma-Post — post-treatment glioma multi-sequence MRI (t1c, t1n, t2f, t2w) with tumor segmentation and primary diagnosis classification (591 timepoints from 203 patients)",
+    "reference": "Mahmoud E, Gass J, Dhemesh Y, Greaser J, Pogorzelski K, Isufi E, Garrett F, Thacker J, Tahon NH, Sinclair J, Layfield L. MU-Glioma Post: A comprehensive dataset of automated MR multi-sequence segmentation and clinical features. Scientific data. 2025 Nov 20;12(1):1847.",
+    "name": "Dataset005_MU_Glioma_Post",
+    "release": "1.0"
+}

Dataset005_MU_Glioma_Post/imagesTr/Pat0003_TP1_0000.nii.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9e4522832cfbe0ef84e314232586f6e1b87d9e81fe28a3aef011b53fc9f057c
+size 5251963

Dataset005_MU_Glioma_Post/imagesTr/Pat0003_TP1_0001.nii.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97a69d2a994bb4a53376dcb8690ba8a53b512e6187d5c6c27c5c7dd39e4971e4
+size 5311862

Dataset005_MU_Glioma_Post/imagesTr/Pat0003_TP1_0002.nii.gz

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+size 5354907

Dataset005_MU_Glioma_Post/imagesTr/Pat0003_TP1_0003.nii.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7a69ca2bbe7ae46caf68b1899c2748098c02189eda6315938cfa19c0673c18a4
+size 5449297

Dataset007_PICAI/dataset.json

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+{
+    "channel_names": {
+        "0": "T2W",
+        "1": "ADC",
+        "2": "HBV"
+    },
+    "labels": {
+        "background": 0,
+        "csPCa": 1
+    },
+    "classification_labels": {
+        "ISUP_grade": {
+            "0": "Benign/Indolent",
+            "1": "ISUP 1",
+            "2": "ISUP 2",
+            "3": "ISUP 3",
+            "4": "ISUP 4",
+            "5": "ISUP 5"
+        }
+    },
+    "numTraining": 1500,
+    "file_ending": ".nii.gz",
+    "description": "PI-CAI \u2014 prostate MRI (T2W, ADC, HBV) with csPCa binary segmentation and ISUP grade classification (1500 cases from 1476 patients across 11 sites)",
+    "reference": "Saha A, Bosma JS, Twilt JJ, et al. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 2024; 25: 879-887.",
+    "license": "CC-BY-NC 4.0",
+    "name": "Dataset007_PICAI",
+    "release": "1.0"
+}

Dataset007_PICAI/imagesTr/10000_1000000_0000.nii.gz

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+oid sha256:ea8c2d39fa6e7bd75eb5e3a48e0c587012b8bbbb7af547fc971e8cd2ac934770
+size 16189455

Dataset007_PICAI/imagesTr/10000_1000000_0001.nii.gz

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+oid sha256:3b5082a2572d3003bf0684a2c3df191501c3cef3b7af0da57455af7a26c51213
+size 17775886

Dataset007_PICAI/imagesTr/10000_1000000_0002.nii.gz

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+oid sha256:e93d7381a469af3a9d723011c00204cbeeed53c0de20ab554b7f576371501e09
+size 6959156