Jacob Gildenblat

According to our database, Jacob Gildenblat authored at least 11 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Dimensionality reduction with strong global structure preservation.

Jacob Gildenblat, Jens Pahnke

Pattern Anal. Appl., March, 2026

2025

Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation.

Jacob Gildenblat, Jens Pahnke

CoRR, March, 2025

2024

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

Jacob Gildenblat, Ofir Hadar

CoRR, 2024

2023

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

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

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2022

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

Eduardo Conde-Sousa, João Vale, Ming Feng, Kele Xu, Yin Wang, Vincenzo Della Mea, David La Barbera, Ehsan Montahaei, Mahdieh Soleymani Baghshah, Andreas Turzynski, Jacob Gildenblat, Eldad Klaiman, Yiyu Hong, Guilherme Aresta, Teresa Araújo, Paulo Aguiar, Catarina Eloy, António Polónia

J. Imaging, 2022

2021

HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization.

Eduardo Conde-Sousa, João Vale, Ming Feng, Kele Xu, Yin Wang, Vincenzo Della Mea, David La Barbera, Ehsan Montahaei, Mahdieh Soleymani Baghshah, Andreas Turzynski, Jacob Gildenblat, Eldad Klaiman, Yiyu Hong, Guilherme Aresta, Teresa Araújo, Paulo Aguiar, Catarina Eloy, António Polónia

CoRR, 2021

2020

Certainty Pooling for Multiple Instance Learning.

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

Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020

2019

Generalising multistain immunohistochemistry tissue segmentation using end-to-end colour deconvolution deep neural networks.

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

IET Image Process., 2019

Self-Supervised Similarity Learning for Digital Pathology.

Jacob Gildenblat, Eldad Klaiman

CoRR, 2019

Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach.

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

Proceedings of the Digital Pathology - 15th European Congress, 2019

2018

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

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

CoRR, 2018