Papers

Multi-modal machine learning approaches for predicting cancer type and Gleason grade leveraging public TCGA data

2024 American Association of Cancer Research

Deep Cellular Embeddings: An Explainable Plug and Play Improvement for Feature Representation in Histopathology

2023 MICCAI, Jacob Gildenblat, Anil Yüce, Samaneh Abbasi-Sureshjani, Konstanty Korski

Identification of MYC -Driven High-Grade B-Cell Lymphoma Using Deep Learning-Based Whole Slide Image Analysis 2023 November Blood

Predicting Cell Of Origin from digitized images of hematoxylin and eosin-stained slides of diffuse large b-cell lymphomas using a cell-based deep-learning model

2023, 17th International Conference on Malignant Lymphoma

HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging

2022

Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies

2020 Sebastian Klein, Jacob Gildenblat, Michaele Angelika Ihle, Sabine Merkelbach-Bruse, Ka-Won Noh, Martin Peifer, Alexander Quaas & Reinhard Büttner

Certainty Pooling for Multiple Instance Learning

2020 Jacob Gildenblat, Ido Ben-Shaul, Zvi Lapp, Eldad Klaiman

Self-Supervised Similarity Learning for Digital Pathology

2019 Jacob Gildenblat, Eldad Klaiman

Prediction of biomarker status, diagnosis and outcome from histology slides using deep learning-based hypothesis free feature extraction

2018 Eldad Klaiman, Jacob Gildenblat, Ido Ben-Shaul, Astrid Heller, Konstanty Korski, Astrid Christina Kiermaier, Fabien Gaire

Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

2018 Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman

Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks

2018 Amal Lahiani, Jacob Gildenblat, Irina Klaman, Nassir Navab, Eldad Klaiman