Change double hyphen to single hyphen in bib item (#9)

diag.bib

@@ -25222,7 +25222,7 @@ @conference{Pole24a
 }
 
 @inproceedings{Pole24b,
-  title = {Histopathobiome -- integrating histopathology and microbiome data via multimodal deep learning},
+  title = {Histopathobiome - integrating histopathology and microbiome data via multimodal deep learning},
   author = {Polejowska, Agata and Boleij, Annemarie and Ciompi, Francesco},
   booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology},
   pages = {203--213},
@@ -25234,7 +25234,7 @@ @inproceedings{Pole24b
   optnote = {DIAG, PATHOLOGY},
   file = {Pole24b.pdf:pdf/Pole24b.pdf:PDF},
   url = {https://proceedings.mlr.press/v254/polejowska24a.html},
-  abstract = {We introduce Histopathobiome, a term representing the integration of histopathology and microbiome data to explore tissue-microbe interactions. Using a dataset of colon biopsy whole-slide images paired with microbiota composition samples, we assess the benefits of combining these modalities to distinguish patients with inflammatory bowel disease (IBD) subtype -- ulcerative colitis (UC) from non-IBD controls. Initially, we evaluate the unimodal performance of state-of-the-art algorithms using vectors representing bacterial species abundances or histopathology slide-level embeddings. We compare single-modality models with bimodal networks with various fusion strategies. Our results prove that histopathology and microbiome data are complementary in UC classification. By demonstrating improved performance over single-modality approaches, we prove that bimodal deep learning models can be used to learn meaningful and interpretable cross-modal tissue-microbe patterns.}
+  abstract = {We introduce Histopathobiome, a term representing the integration of histopathology and microbiome data to explore tissue-microbe interactions. Using a dataset of colon biopsy whole-slide images paired with microbiota composition samples, we assess the benefits of combining these modalities to distinguish patients with inflammatory bowel disease (IBD) subtype - ulcerative colitis (UC) from non-IBD controls. Initially, we evaluate the unimodal performance of state-of-the-art algorithms using vectors representing bacterial species abundances or histopathology slide-level embeddings. We compare single-modality models with bimodal networks with various fusion strategies. Our results prove that histopathology and microbiome data are complementary in UC classification. By demonstrating improved performance over single-modality approaches, we prove that bimodal deep learning models can be used to learn meaningful and interpretable cross-modal tissue-microbe patterns.}
 }
 
 @article{Pomp15,