53 citations · 95 across the 6 of their papers we have counts for
8 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…
HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization
Eduardo Conde-Sousa, João Vale, Ming Feng +15
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount import…
Certainty Pooling for Multiple Instance Learning
Jacob Gildenblat, Ido Ben-Shaul, Zvi Lapp +1
Multiple Instance Learning is a form of weakly supervised learning in which the data is arranged in sets of instances called bags with one label assigned per bag. The bag level cla…
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…
Self-Supervised Similarity Learning for Digital Pathology
Jacob Gildenblat, Eldad Klaiman
Using features extracted from networks pretrained on ImageNet is a common practice in applications of deep learning for digital pathology. However it presents the downside of missi…
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…