10 citations · 10 across the 4 of their papers we have counts for
5 papers
B-Cos Aligned Transformers Learn Human-Interpretable Features
Manuel Tran, Amal Lahiani, Yashin Dicente Cid +7
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology. However, domain experts are still reluctant to use these models d…
Training Transitive and Commutative Multimodal Transformers with LoReTTa
Manuel Tran, Yashin Dicente Cid, Amal Lahiani +3
Training multimodal foundation models is challenging due to the limited availability of multimodal datasets. While many public datasets pair images with text, few combine images wi…
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
Amal Lahiani, Nassir Navab, Shadi Albarqouni +1
Style transfer is a field with growing interest and use cases in deep learning. Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images o…
Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
Amal Lahiani, Jacob Gildenblat, Irina Klaman +3
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has be…
Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks
Amal Lahiani, Jacob Gildenblat, Irina Klaman +2
A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumor biopsies. Different cell types tend to mi…