Update README.md

README.md

@@ -1,48 +1,111 @@
----
-license: cc-by-nc-sa-4.0
-dataset_info:
-  features:
-  - name: patient
-    dtype: string
-  - name: image
-    dtype:
-      image:
-        mode: RGB
-  - name: instances
-    sequence:
-      image:
-        mode: '1'
-  - name: categories
-    sequence:
-      class_label:
-        names:
-          '0': Ambiguous
-          '1': Epithelial
-          '2': Lymphocyte
-          '3': Macrophage
-          '4': Neutrophil
-  - name: tissue
-    dtype:
-      class_label:
-        names:
-          '0': Breast
-          '1': Kidney
-          '2': Lung
-          '3': Prostate
-  splits:
-  - name: train
-    num_bytes: 217460437.0
-    num_examples: 209
-  - name: test
-    num_bytes: 93787071.0
-    num_examples: 101
-  download_size: 303503853
-  dataset_size: 311247508.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+---
+license: cc-by-nc-sa-4.0
+dataset_info:
+  features:
+  - name: patient
+    dtype: string
+  - name: image
+    dtype:
+      image:
+        mode: RGB
+  - name: instances
+    sequence:
+      image:
+        mode: '1'
+  - name: categories
+    sequence:
+      class_label:
+        names:
+          '0': Ambiguous
+          '1': Epithelial
+          '2': Lymphocyte
+          '3': Macrophage
+          '4': Neutrophil
+  - name: tissue
+    dtype:
+      class_label:
+        names:
+          '0': Breast
+          '1': Kidney
+          '2': Lung
+          '3': Prostate
+  splits:
+  - name: train
+    num_bytes: 217460437
+    num_examples: 209
+  - name: test
+    num_bytes: 93787071
+    num_examples: 101
+  download_size: 303503853
+  dataset_size: 311247508
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+task_categories:
+- image-segmentation
+task_ids:
+- instance-segmentation
+language:
+- en
+tags:
+- medical
+- cell nuclei
+- H&E
+pretty_name: MoNuSAC
+paperswithcode_id: monusac
+size_categories:
+- n<1K
+---
+
+# MoNuSAC
+
+## Dataset Description
+
+- **Homepage:** [MoNuSAC 2020](https://monusac-2020.grand-challenge.org/Home/)
+- **Leaderboard:** [Medical Image Segmentation](https://paperswithcode.com/sota/medical-image-segmentation-on-monusac)
+
+## Description
+
+Dataset contains images from four distinct organs: Lung, Prostate, Kidney, and Breast. The images vary in size from 100 to 2000 px and are captured at 40x magnification, though the exact micrometers-per-pixel resolution is not provided. MoNuSAC provides annotations for four types of cell nuclei. The dataset is divided into a training set and a test set. The training set comprises 209 image patches and 31,411 annotated nuclei from 46 patients, while the test set includes 101 image patches and 15,498 annotated nuclei from 25 patients.
+
+## Dataset Structure
+
+The dataset is organized into `train` and `test` splits, consistent with the original dataset structure. Each split contains data in a tabular format with the following four columns:
+
+- **`patient`**: The patient id.
+- **`image`**: The RGB tile of the sample.
+- **`instances`**: A list of nuclei instances. Each instance represents exactly one nucleus and is in binary format (`1` - nucleus, `0` - background)
+- **`categories`**: An integer class label for each nucleus, corresponding to one of the following categories:
+  0. Ambiguous
+  1. Epithelial
+  2. Lymphocyte
+  3. Macrophage
+  4. Neutrophil
+
+  The ambiguous regions are those that have very faint nuclei with fuzzy boundaries, nuclei for which class assignments were difficult (high chances of incorrect manual labeling) and other nuclei not included in this challenge (endothelial cells, fibroblasts, etc.).
+  
+- **`tissue`**: The integer tissue type from which the sample originates, belonging to one of these categories:
+  0. Breast
+  1. Kidney
+  2. Lung
+  3. Prostate
+
+## Citation
+
+```bibtex
+@article{9446924,
+  author={Verma, Ruchika and Kumar, Neeraj and Patil, Abhijeet and Kurian, Nikhil Cherian and Rane, Swapnil and Graham, Simon and Vu, Quoc Dang and Zwager, Mieke and Raza, Shan E. Ahmed and Rajpoot, Nasir and Wu, Xiyi and Chen, Huai and Huang, Yijie and Wang, Lisheng and Jung, Hyun and Brown, G. Thomas and Liu, Yanling and Liu, Shuolin and Jahromi, Seyed Alireza Fatemi and Khani, Ali Asghar and Montahaei, Ehsan and Baghshah, Mahdieh Soleymani and Behroozi, Hamid and Semkin, Pavel and Rassadin, Alexandr and Dutande, Prasad and Lodaya, Romil and Baid, Ujjwal and Baheti, Bhakti and Talbar, Sanjay and Mahbod, Amirreza and Ecker, Rupert and Ellinger, Isabella and Luo, Zhipeng and Dong, Bin and Xu, Zhengyu and Yao, Yuehan and Lv, Shuai and Feng, Ming and Xu, Kele and Zunair, Hasib and Hamza, Abdessamad Ben and Smiley, Steven and Yin, Tang-Kai and Fang, Qi-Rui and Srivastava, Shikhar and Mahapatra, Dwarikanath and Trnavska, Lubomira and Zhang, Hanyun and Narayanan, Priya Lakshmi and Law, Justin and Yuan, Yinyin and Tejomay, Abhiroop and Mitkari, Aditya and Koka, Dinesh and Ramachandra, Vikas and Kini, Lata and Sethi, Amit},
+  journal={IEEE Transactions on Medical Imaging}, 
+  title={MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge}, 
+  year={2021},
+  volume={40},
+  number={12},
+  pages={3413-3423},
+  keywords={Annotations;Image segmentation;Tumors;Computer architecture;Training;Task analysis;Semantics;Multi-organ dataset;nucleus classification;computational pathology;instance segmentation;panoptic quality},
+  doi={10.1109/TMI.2021.3085712}
+}
+```