Search Results for author:
Found 8 papers, 0 papers with code
B-Cos Aligned Transformers Learn Human-Interpretable Features
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology.
Training Transitive and Commutative Multimodal Transformers with LoReTTa
We introduce LoReTTa (Linking mOdalities with a tRansitive and commutativE pre-Training sTrAtegy) to address this understudied problem.
HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year.
Certainty Pooling for Multiple Instance Learning
The bag level class prediction is derived from the multiple instances through application of a permutation invariant pooling operator on instance predictions or embeddings.
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically validated interpretability.
Self-Supervised Similarity Learning for Digital Pathology
Using features extracted from networks pretrained on ImageNet is a common practice in applications of deep learning for digital pathology.
Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology.
Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks
A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumor biopsies.
