Jacob Gildenblat
About me Papers

Papers

  • Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features

  • 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