From 137b4468e1b76fdb5816e726e6bd42c61dc195af Mon Sep 17 00:00:00 2001
From: drepeeters <dre.peeters@hotmail.com>
Date: Thu, 7 Dec 2023 13:53:42 +0100
Subject: [PATCH] Add Geijs21

---
 diag.bib | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/diag.bib b/diag.bib
index 3a86627..d6eab52 100644
--- a/diag.bib
+++ b/diag.bib
@@ -8450,20 +8450,20 @@ @mastersthesis{Geij19
   journal = {Master thesis},
 }
 
-@inproceedings{Geij21,
-  author = {Geijs, DJ and Pinckaers, H and Amir, AL and Litjens, GJS},
-  booktitle = MI,
-  title = {End-to-end classification on basal-cell carcinoma histopathology whole-slides images},
-  doi = {10.1117/12.2581042},
-  pages = {1160307},
-  series = SPIE,
-  volume = {11603},
-  abstract = {The high incidence of BCC skin cancer caused that the amount of work for pathologists has risen to unprecedented levels. Acquiring outlined annotations for training deep learning models classifying BCC is often tedious and time consuming. End-to-end learning provides relief in labelling data by using a single label to predict an clinical outcome. We compared multiple-instance-learning (MIL) and a streaming performance for detecting BCC in 420 slides collected from 72 BCC positive patients. This resulted in an ROC with AUC of 0.96 and 0.98 for respectively streaming and MIL. Saliency and probability maps showed that both methods were capable of classifying classifying BCC in an end-to-end way with single labels.},
-  file = {Geij21.pdf:pdf\\Geij21.pdf:PDF},
-  year = {2021},
-  ss_id = {4ea1f801a7d14b2bf284fef25d83bcc222c01629},
+@InProceedings{Geij21,
+  author     = {Geijs, Daan and Pinckaers, H and Amir, AL and Litjens, GJS},
+  booktitle  = MI,
+  title      = {End-to-end classification on basal-cell carcinoma histopathology whole-slides images},
+  doi        = {10.1117/12.2581042},
+  pages      = {1160307},
+  series     = {#SPIE#},
+  volume     = {11603},
+  abstract   = {The high incidence of BCC skin cancer caused that the amount of work for pathologists has risen to unprecedented levels. Acquiring outlined annotations for training deep learning models classifying BCC is often tedious and time consuming. End-to-end learning provides relief in labelling data by using a single label to predict an clinical outcome. We compared multiple-instance-learning (MIL) and a streaming performance for detecting BCC in 420 slides collected from 72 BCC positive patients. This resulted in an ROC with AUC of 0.96 and 0.98 for respectively streaming and MIL. Saliency and probability maps showed that both methods were capable of classifying classifying BCC in an end-to-end way with single labels.},
   all_ss_ids = {['4ea1f801a7d14b2bf284fef25d83bcc222c01629']},
-  gscites = {2},
+  file       = {Geij21.pdf:pdf\\Geij21.pdf:PDF},
+  gscites    = {2},
+  ss_id      = {4ea1f801a7d14b2bf284fef25d83bcc222c01629},
+  year       = {2021},
 }
 
 @article{Geld18,