Jakob Nikolas Kather
Artificial Intelligence · 2015–2023
A researcher in the Science corpus: 12 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 167 citations into it
- Computer Vision and Pattern Recognition — 98 citations into it
- Biophysics — 45 citations into it
Whom Jakob Nikolas Kather trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Lothar Rudi Schad
- Frank G. Zöllner
- M. J. Turner
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Adrian Baddeley
- A. Minchinton
- Stephen Westland
- Xianghua Xie
Who trusts Jakob Nikolas Kather overall
- Artem Shmatko
- Jiancheng Yang
- J. A. van der Laak
- Richard J. Chen
- Ming Y. Lu
- Geert J. S. Litjens
- B. Bhinder
- N. Ghaffari Laleh
- Wan Zhu
- Omid Nejati Manzari
Papers from Jakob Nikolas Kather's perspective
Ordered by how strongly the researchers Jakob Nikolas Kather trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- The future landscape of large language models in medicine (2023) — 969 citations
- Denoising diffusion probabilistic models for 3D medical image generation (2023) — 336 citations
- Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022) — 405 citations
- The impact of site-specific digital histology signatures on deep learning model accuracy and bias (2021) — 295 citations
- Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. (2020) — 318 citations
- Deep learning in cancer pathology: a new generation of clinical biomarkers (2020) — 619 citations
- Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer (2019) — 1,260 citations
- Pan-cancer image-based detection of clinically actionable genetic alterations (2019) — 600 citations
- Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study (2019) — 1,042 citations
- Multi-class texture analysis in colorectal cancer histology (2016) — 584 citations
- New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images (2015) — 19 citations
- Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images (2015) — 66 citations