Include citations for datasets

README.md

@@ -29,9 +29,21 @@ For more details, please refer to the license file linked above.
 
 Videos from the following datasets were used for finetuning:
 
-* [Heidelberg colorectal (HeiCo)](https://doi.org/10.7303/syn21903917)
-* [Cholec80](http://camma.u-strasbg.fr/datasets/)
-* [MultiBypass140](https://github.com/CAMMA-public/MultiBypass140)
+* [**Heidelberg colorectal (HeiCo)**](https://doi.org/10.7303/syn21903917), as described in
+  * Maier-Hein, L., Wagner, M., Ross, T., Reinke, A., Bodenstedt, S., Full, P. M., ... & Müller-Stich, B. P. (2021).
+    Heidelberg colorectal data set for surgical data science in the sensor operating room.
+    [Scientific data, 8(1), 1-11](https://doi.org/10.1038/s41597-021-00882-2)
+  * Roß, T., Reinke, A., Full, P. M., Wagner, M., Kenngott, H., Apitz, M., ... & Maier-Hein, L. (2021). Comparative validation
+    of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge.
+    [Medical image analysis, 70, 101920](https://doi.org/10.1016/j.media.2020.101920)
+* [**Cholec80**](http://camma.u-strasbg.fr/datasets/), as described in
+
+    A.P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, N. Padoy, EndoNet: A Deep Architecture for Recognition
+    Tasks on Laparoscopic Videos, IEEE Transactions on Medical Imaging (TMI), [arXiv preprint](http://arxiv.org/abs/1602.03012), 2017
+* [**MultiBypass140**](https://github.com/CAMMA-public/MultiBypass140), as described in
+
+    Lavanchy, J.L., Ramesh, S., Dall’Alba, D. et al. Challenges in multi-centric generalization: phase and step recognition
+    in Roux-en-Y gastric bypass surgery. [Int J CARS 19, 2249–2257 (2024)](https://doi.org/10.1007/s11548-024-03166-3)
 
 We are grateful to all authors for creating and publicly releasing these datasets!