From a0338dd3dc9c44c6f65c35a530f44f55cbd0b920 Mon Sep 17 00:00:00 2001 From: jvwilliams23 Date: Sun, 22 Jan 2023 20:55:20 +0000 Subject: [PATCH] fix: remove issue in bibtex --- paper/paper.bib | 6 ------ paper/paper.md | 5 ----- 2 files changed, 11 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index caa51b0..50f0565 100644 --- a/paper/paper.bib +++ b/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}, diff --git a/paper/paper.md b/paper/paper.md index 05eadb2..070f016 100644 --- a/paper/paper.md +++ b/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 \ No newline at end of file