update

diag.bib

@@ -14732,7 +14732,7 @@ @conference{Jong22
 }
 
 @conference{Jong24,
-  author = {Siem de Jong, Marie Louise Groot, Roel L. J. Verhoeven, Erik H. F. M. van der Heijden, Francesco Ciompi},
+  author = {Siem de Jong and Marie Louise Groot and Roel L. J. Verhoeven and Erik H. F. M. van der Heijden and Francesco Ciompi},
   booktitle = {Medical Imaging with Deep Learning 2024},
   title = {Weakly supervised lung cancer detection on label-free intraoperative microscopy with higher harmonic generation},
   abstract = {Higher harmonic generation microscopy (HHGM) enables label-free on-site imaging of fresh tissue, potentially allowing a new means of pathology assessment for disease diagnosis. We investigate the potential of using self-supervised learning (SSL) in combination with weakly-supervised, attention-based, clustering constrained multiple instance learning (CLAM) to detect lung cancer in HHGM images. First, we tailor encoders to HHGM-specific data domain via both SimCLR and DINO SSL. Second, we train a CLAM classifier with and without an SSL feature extractor on 100 HHGM images acquired during bronchoscopy procedures. We show that SSL pre-training with random initialization and CLAM are beneficial to intraoperatively detect lung cancer in HHGM images.},
@@ -24753,7 +24753,7 @@ @article{Pola23
 }
 
 @conference{Pole24,
-  author = {Agata Polejowska, Fazael Ayatollahi, Ayse Selcen Oguz Erdogan, Francesco Ciompi, Annemarie Boleij},
+  author = {Agata Polejowska and Fazael Ayatollahi and Ayse Selcen Oguz Erdogan and Francesco Ciompi and Annemarie Boleij},
   booktitle = {Medical Imaging with Deep Learning 2024},
   title = {Spirochetosis detection in colon histopathology images via fine-tuning and boosting techniques using foundation models},
   abstract = {Spirochetes are bacteria that can be found on the boundaries of colon epithelial tissue, causing several diseases ranging from spirochetosis, inflammatory bowel disease, to cancer. Despite their relevance, spirochetes often remain undetected in histological analysis. We propose the first computational pathology approach to characterize spirochetes, leveraging prior spatial knowledge to detect spirochetes in whole-slide images of colon polyps and biopsies, and differentiate these bacteria as belonging to normal or abnormal tissue. We focus on transfer learning by fine-tuning state-of-the-art computational pathology foundation models and by training an additional XGBoost classifier on downstream tasks.},
@@ -31170,7 +31170,7 @@ @article{Stur22
 }
 
 @conference{Stur24,
-  author = {Bart Sturm, Petra Lock, Johan Westerga, Willeke Blokx, Jeroen van der Laak},
+  author = {Bart Sturm and Petra Lock and Johan Westerga and Willeke Blokx and Jeroen van der Laak},
   booktitle = {European Congress on Digital Pathology},
   title = {Deep learning predicts the effect of neo-adjuvant chemotherapy for patients with triple negative breast cancer},
   abstract = {Introduction
@@ -31864,7 +31864,7 @@ @article{Terh21
 }
 
 @conference{Tess24,
-  author = {Leslie Tessier and Cristina González-Gonzalo and David Tellez and Wouter Bulten and Maschenka Balkenhol and Jeroen van der Laak},
+  author = {Leslie Tessier and Cristina González-Gonzalo and David Tellez and Wouter Bulten and Maschenka Balkenhol  Jeroen van der Laak},
   booktitle = {European Congress on Digital Pathology},
   title = {Large-scale validation of AI-assisted mitosis counting in breast cancer},
   abstract = {Introduction