Update README.md

README.md

@@ -37,29 +37,18 @@ tags:
 size_categories:
 - 100K<n<1M
 license: cc0-1.0
+paperswithcode_id: pcam
 ---
 
-[![DOI](https://zenodo.org/badge/DOI/10.1007/978-3-030-00934-2_24.svg)](https://doi.org/10.1007/978-3-030-00934-2_24)
-
 ![](https://github.com/basveeling/pcam/raw/master/pcam.jpg)
 
 # PatchCamelyon (PCam)
 
-**Homepage**: https://github.com/basveeling/pcam \
-**Publication Date**: 2018-09-26 \
-**License**: [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) \
-**Citation**:
-```bibtex
-@ARTICLE{Veeling2018-qh,
-  title         = "Rotation Equivariant {CNNs} for Digital Pathology",
-  author        = "Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max",
-  month         =  jun,
-  year          =  2018,
-  archivePrefix = "arXiv",
-  primaryClass  = "cs.CV",
-  eprint        = "1806.03962"
-}
-```
+## Dataset Description
+
+- **Homepage**: [github.com:basveeling/pcam](https://github.com/basveeling/pcam)
+- **DOI**: https://doi.org/10.1007/978-3-030-00934-2_24
+- **Publication Date**: 2018-09-26
 
 ## Description
 
@@ -85,3 +74,17 @@ A positive label indicates that the center 32x32px region of a patch contains at
 PCam is derived from the Camelyon16 Challenge, which contains 400 H\&E stained WSIs of sentinel lymph node sections. The slides were acquired and digitized at 2 different centers  using a 40x objective (resultant pixel resolution of 0.243 microns). We undersample this at 10x to increase the field of view.
 We follow the train/test split from the Camelyon16 challenge, and further hold-out 20% of the train WSIs for the validation set. To prevent selecting background patches, slides are converted to HSV, blurred, and patches filtered out if maximum pixel saturation lies below 0.07 (which was validated to not throw out tumor data in the training set).
 The patch-based dataset is sampled by iteratively choosing a WSI and selecting a positive or negative patch with probability _p_. Patches are rejected following a stochastic hard-negative mining scheme with a small CNN, and _p_ is adjusted to retain a balance close to 50/50.
+
+## Citation
+
+```bibtex
+@ARTICLE{Veeling2018-qh,
+  title         = "Rotation Equivariant {CNNs} for Digital Pathology",
+  author        = "Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max",
+  month         =  jun,
+  year          =  2018,
+  archivePrefix = "arXiv",
+  primaryClass  = "cs.CV",
+  eprint        = "1806.03962"
+}
+```