Add dataset card

README.md

@@ -1,58 +1,93 @@
 ---
-dataset_info:
-  features:
-  - name: sample_id
-    dtype: string
-  - name: tissue
-    dtype:
-      class_label:
-        names:
-          '0': BC
-          '1': Bladder
-          '2': Lung
-  - name: subset
-    dtype: string
-  - name: ihc
-    dtype: image
-  - name: hematoxylin
-    dtype: image
-  - name: dapi
-    dtype: image
-  - name: lap2
-    dtype: image
-  - name: marker
-    dtype: image
-  - name: seg_mask
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 819179421
-    num_examples: 575
-  - name: validation
-    num_bytes: 130744578
-    num_examples: 91
-  - name: test
-    num_bytes: 825956215
-    num_examples: 598
-  - name: bc_train
-    num_bytes: 592905848
-    num_examples: 385
-  - name: bc_validation
-    num_bytes: 98729586
-    num_examples: 66
-  download_size: 2467676469
-  dataset_size: 2467515648
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-  - split: bc_train
-    path: data/bc_train-*
-  - split: bc_validation
-    path: data/bc_validation-*
+license: cc-by-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - medical
+  - histopathology
+  - immunohistochemistry
+  - ihc
+  - multiplex-immunofluorescence
+  - ki67
+  - cell-segmentation
+  - deepliif
+pretty_name: DeepLIIF
+size_categories:
+  - 1K<n<10K
 ---
+
+# DeepLIIF (Deep Learning-Inferred Immunofluorescence)
+
+Co-registered IHC (Ki67-DAB brightfield) and multiplex immunofluorescence (mpIF)
+patches with cell-level segmentation + classification ground truth for
+quantification of clinical pathology slides.
+
+Source paper: Ghahremani et al., "Deep learning-inferred multiplex
+immunofluorescence for immunohistochemical image quantification,"
+*Nature Machine Intelligence* 4(4):401-412, 2022.
+DOI: [10.1038/s42256-022-00471-x](https://doi.org/10.1038/s42256-022-00471-x).
+Zenodo: <https://zenodo.org/records/4751737>.
+
+## Overview
+- **Modality:** Histopathology - IHC brightfield + co-registered mpIF (DAPI, Lap2, Ki67 marker)
+- **Patch size:** 512x512 each (originals are 3072x512 PNGs concatenating six panels)
+- **Tissue:** Bladder + Lung (main DeepLIIF), Breast carcinoma (BC-DeepLIIF subset)
+- **Total samples:** 1,715
+  - `train` (DeepLIIF): 575
+  - `validation` (DeepLIIF): 91
+  - `test` (DeepLIIF): 598
+  - `bc_train` (BC-DeepLIIF, breast): 385
+  - `bc_validation` (BC-DeepLIIF, breast): 66
+
+## Columns
+| Column | Type | Notes |
+|---|---|---|
+| `sample_id` | string | Original PNG stem |
+| `tissue` | ClassLabel(3) | `0=BC`, `1=Bladder`, `2=Lung` |
+| `subset` | string | `DeepLIIF` (main) or `BC-DeepLIIF` |
+| `ihc` | Image (RGB) | Input - 512x512 brightfield Ki67-DAB IHC |
+| `hematoxylin` | Image (RGB) | Aux target - reconstructed hematoxylin channel |
+| `dapi` | Image (RGB) | Aux target - mpIF DAPI nuclear stain |
+| `lap2` | Image (RGB) | Aux target - mpIF Lap2 nuclear-envelope stain |
+| `marker` | Image (RGB) | Aux target - mpIF Ki67 marker channel |
+| `seg_mask` | Image (RGB) | **Ground truth** - red=Ki67+ cell, blue=Ki67- cell, green=boundary, black=background |
+
+## Ground Truth
+The `seg_mask` column is the canonical GT. It was generated by combining
+per-modality instance segmentations from mpIF DAPI + Lap2 + Hematoxylin + IHC,
+with instance boundaries initialized by the ImPartial interactive framework on
+DAPI. Cells are then classified red/blue based on Ki67 marker positivity.
+
+For binary semantic segmentation (nucleus vs background) treat (red OR blue)
+pixels as foreground. For positive-vs-negative classification, decode red and
+blue channels separately.
+
+## Derivation
+Each source PNG was sliced column-wise into six 512x512 panels:
+- Columns [0:512]       -> `ihc`
+- Columns [512:1024]    -> `hematoxylin`
+- Columns [1024:1536]   -> `dapi`
+- Columns [1536:2048]   -> `lap2`
+- Columns [2048:2560]   -> `marker`
+- Columns [2560:3072]   -> `seg_mask`
+
+No other preprocessing.
+
+## License
+CC BY 4.0 (dataset, per Zenodo record 4751737). The DeepLIIF code repo is
+Apache 2.0 with Commons Clause; that license applies only to code/models, not
+to this imaging data.
+
+## Citation
+```bibtex
+@article{ghahremani2022deep,
+  title={Deep learning-inferred multiplex immunofluorescence for
+         immunohistochemical image quantification},
+  author={Ghahremani, Parmida and Li, Yanyun and Kaufman, Arie and
+          Vanguri, Rami and Greenwald, Noah and Angelo, Michael and
+          Hollmann, Travis J and Nadeem, Saad},
+  journal={Nature Machine Intelligence},
+  volume={4}, number={4}, pages={401--412}, year={2022},
+  doi={10.1038/s42256-022-00471-x}
+}
+```