282 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Karolinska InstitutetSE28 papers
- KTH Royal Institute of TechnologySE10 papers
- Stockholm South General HospitalSE4 papers
- University of BoråsSE4 papers
- King's College LondonGB3 papers
- Max Planck Institute for Developmental BiologyDE3 papers
- New York UniversityUS3 papers
- Stanford UniversityUS3 papers
- Uppsala UniversitySE3 papers
- Corporació Sanitària Parc TaulíES2 papers
- Gdańsk Medical UniversityPL2 papers
- Hospital Universitari Germans Trias i PujolES2 papers
5 papers · 1 filter
Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel Transport
Jingru Fu, Yuqi Zheng, Neel Dey +2
Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or di…
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Iulian Emil Tampu, Per Nyman, Christoforos Spyretos +7
Brain tumors are the most common solid tumors in children and young adults, but the scarcity of large histopathology datasets has limited the application of computational pathology…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks
Philippe Weitz, Yinxi Wang, Kimmo Kartasalo +5
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics. However, molecular profiling remains costly and resource…
US-net for robust and efficient nuclei instance segmentation
Zhaoyang Xu, Faranak Sobhani, Carlos Fernandez Moro +1
We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images. The proposed framework integrates the nuclei detection and…