Add conference paper Agata and update workshop paper Francesco (#8)

diag.bib

@@ -5683,17 +5683,16 @@ @article{Chav93
   gscites = {16},
 }
 
-@article{Chel22,
+@inproceedings{Chel22,
   author = {Chelebian, Eduard and Ciompi, Francesco and W\"{a}hlby, Carolina},
   title = {Seeded iterative clustering for histology region identification},
   doi = {10.48550/ARXIV.2211.07425},
+  booktitle = {Medical Imaging Meets NeurIPS Workshop - 36th Conference on Neural Information Processing Systems (NeurIPS)},
   year = {2022},
-  abstract = {Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make. Deep learning models for segmentation are a way to alleviate the process, but require large amounts of training data, training times and computing power. To address these issues, we present seeded iterative clustering to produce a coarse segmentation densely and at the whole slide level. The algorithm uses precomputed representations as the clustering space and a limited amount of sparse interactive annotations as seeds to iteratively classify image patches. We obtain a fast and effective way of generating dense annotations for whole slide images and a framework that allows the comparison of neural network latent representations in the context of transfer learning.},
   url = {https://arxiv.org/abs/2211.07425},
   file = {Chel22.pdf:pdf\\Chel22.pdf:PDF},
+  abstract = {Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make. Deep learning models for segmentation are a way to alleviate the process, but require large amounts of training data, training times and computing power. To address these issues, we present seeded iterative clustering to produce a coarse segmentation densely and at the whole slide level. The algorithm uses precomputed representations as the clustering space and a limited amount of sparse interactive annotations as seeds to iteratively classify image patches. We obtain a fast and effective way of generating dense annotations for whole slide images and a framework that allows the comparison of neural network latent representations in the context of transfer learning.},
   optnote = {DIAG, RADIOLOGY},
-  journal = {5},
-  automatic = {yes},
   all_ss_ids = {['892d4f3ee2cc9ff2dcf1c308fae68473dcf787d2']},
   gscites = {0},
 }
@@ -25213,7 +25212,7 @@ @article{Pola23
   gscites = {0},
 }
 
-@conference{Pole24,
+@conference{Pole24a,
   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},
@@ -25222,6 +25221,22 @@ @conference{Pole24
   year = {2024},
 }
 
+@inproceedings{Pole24b,
+  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},
+  year = {2024},
+  volume = {254},
+  series = {Proceedings of Machine Learning Research},
+  month = {10},
+  publisher = {PMLR},
+  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.}
+}
+
 @article{Pomp15,
   author = {Pompe, Esther and van Rikxoort, Eva M. and Schmidt, Michael and R\"uhaak, Jan and Gallardo-Estrella, L. and Vliegenthart, Rozemarijn and Oudkerk, Matthijs and de Koning, Harry J. and van Ginneken, Bram and de Jong, Pim A. and Lammers, Jan-Willem J. and Mohamed Hoesein, Firdaus A A.},
   title = {Parametric response mapping adds value to current computed tomography biomarkers in diagnosing chronic obstructive pulmonary disease},