Hannah Gilmore

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 42 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 63 citations into it; #16,181 of 21,575 by trust here, #5,711 by citations
• Artificial Intelligence — 56 citations into it; #22,542 of 35,345 by trust here, #6,664 by citations
• Radiology, Nuclear Medicine and Imaging — 13 citations into it; #2,671 of 3,265 by trust here, #664 by citations
• Representation & Self-Supervised Learning — 13 citations into it

Whom Hannah Gilmore trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Yann LeCun
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Koray Kavukcuoglu
6. Ronan Collobert
7. Ilya Sutskever
8. L. Eon Bottou
9. Anant Madabhushi
10. Diederik P. Kingma

Who trusts Hannah Gilmore overall

1. Anant Madabhushi
2. Andrew Janowczyk

Papers from Hannah Gilmore's perspective

Ordered by how strongly the researchers Hannah Gilmore trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014) — 632 citations
• HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides (2019) — 325 citations
• Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017) — 479 citations
• A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images (2016) — 458 citations
• Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images (2015) — 815 citations
• Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features (2014) — 334 citations