Faisal Mahmood
Artificial Intelligence · Nature Biomedical Engineering · 2017–2023
A researcher in the Science corpus: 11 papers; trusts 335 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 351 citations into it; #18,866 of 35,345 by trust here, #6,911 by citations
- Computer Vision and Pattern Recognition — 193 citations into it; #15,745 of 21,575 by trust here, #7,421 by citations
- Radiology, Nuclear Medicine and Imaging — 123 citations into it
- Biophysics — 82 citations into it; #958 of 1,196 by trust here, #337 by citations
- Oncology — 63 citations into it
- Cancer Research — 53 citations into it
- Molecular Biology — 50 citations into it
- Model Architectures — 34 citations into it
Whom Faisal Mahmood trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Individualized Treat
- Yann LeCun
- Ross B. Girshick
- Ilya Sutskever
- Olaf Ronneberger
Who trusts Faisal Mahmood overall
- Garrett Wilson
- Richard J. Chen
- Adnan Qayyum
- Diane Joyce Cook
- Khushboo Munir
- Sergey I. Nikolenko
- Junaid Qadir
- Ming Y. Lu
- Paul Hand
- Daniel Borders
Papers from Faisal Mahmood's perspective
Ordered by how strongly the researchers Faisal Mahmood 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018) — 331 citations
- Artificial intelligence for digital and computational pathology (2023) — 348 citations
- Algorithm fairness in artificial intelligence for medicine and healthcare (2023) — 550 citations
- Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning (2022) — 716 citations
- Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning (2022) — 577 citations
- Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images (2021) — 406 citations
- Synthetic data in machine learning for medicine and healthcare (2021) — 694 citations
- Data-efficient and weakly supervised computational pathology on whole-slide images (2020) — 2,191 citations
- AI-based pathology predicts origins for cancers of unknown primary (2020) — 622 citations
- Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis (2019) — 654 citations
- Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training (2017) — 266 citations