fix: remove issue in bibtex

paper/paper.bib

@@ -10,7 +10,6 @@ @inproceedings{bhalodia2018deepssm
 @article{ferrarini2007games,
  abstract = {This paper presents a new framework for shape modeling and analysis, rooted in the pattern recognition theory and based on artificial neural networks. Growing and adaptive meshes (GAMEs) are introduced: GAMEs combine the self-organizing networks which grow when require (SONGWR) algorithm and the Kohonen’s self-organizing maps (SOMs) in order to build a mesh representation of a given shape and adapt it to instances of similar shapes. The modeling of a surface is seen as an unsupervised clustering problem, and tackled by using SONGWR (topology-learning phase). The point correspondence between point distribution models is granted by adapting the original model to other instances: the adaptation is seen as a classification task and performed accordingly to SOMs (topology-preserving phase). We thoroughly evaluated our method on challenging synthetic datasets, with different levels of noise and shape variations. Finally, we describe its application to the analysis of a challenging medical dataset. Our method proved to be reproducible, robust to noise, and capable of capturing real variations within and between groups of shapes.},
  author = {Ferrarini, Luca and Olofsen, Hans and Palm, Walter M and Van Buchem, Mark A and Reiber, Johan HC and Admiraal-Behloul, Faiza},
- doi = {https://doi.org/10.1016/j.media.2007.03.006},
  journal = {Medical image analysis},
  number = {3},
  pages = {302--314},
@@ -41,11 +40,6 @@ @incollection{cates2017shapeworks
 
 @article{baka20112D3D,
  author = {Baka, Nora and Kaptein, Bart L and de Bruijne, Marleen and van Walsum, Theo and Giphart, JE and Niessen, Wiro J and Lelieveldt, Boudewijn PF},
- comment = {Highlights
-Reconstruction of the 3D femur shape from two calibrated X-rays with minimal user interaction. 
-Combines the benefits of a 3D similarity metric with an automatic edge selection scheme. 
-An orientation based correspondence weighting provides robustness w.r.t. noise. 
-Robustness w.r.t. the FOV size enables fitting an SSM of the whole femur to knee X-rays, exploiting all information present in the FOV.},
  doi = {10.1016/j.media.2011.04.001},
  journal = {Medical image analysis},
  number = {6},

paper/paper.md

@@ -128,9 +128,4 @@ In this case, the change in shape and appearance are mainly due to how the lung
 \section*{Acknowledgement}
 JW was funded by a 2019 PhD Scholarship from the Carnegie-Trust for the Universities of Scotland. 
 
-\bibliography{pyssam_refs}
-
-
 # References
-
-[def]: figures/code-schematic.pdf