M. Feldman

Computer Vision and Pattern Recognition · IEEE International Symposium on Biomedical Imaging · 2008–2019

A researcher in the Science corpus: 8 papers; trusts 6 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it; #11,868 of 21,575 by trust here, #6,206 by citations
• Artificial Intelligence — 41 citations into it; #18,818 of 35,345 by trust here, #7,241 by citations
• Representation & Self-Supervised Learning — 9 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it; #2,777 of 3,265 by trust here, #1,054 by citations

Whom M. Feldman trusts overall

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

1. Serge Beucher
2. Yoshua Bengio
3. Yann LeCun
4. Corinna Cortes
5. Robert Martin Haralick
6. Geoffrey E. Hinton
7. Vladimir Naumovich Vapnik
8. Koray Kavukcuoglu
9. Alex Krizhevsky
10. Anant Madabhushi

Who trusts M. Feldman overall

1. Jocelyn Barker
2. Angel Cruz-Roa

Papers from M. Feldman's perspective

Ordered by how strongly the researchers M. Feldman 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014) — 632 citations
• Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology (2008) — 364 citations
• HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides (2019) — 325 citations
• A Protein-Truncating HSD17B13 Variant and Protection from Chronic Liver Disease (2018) — 665 citations
• Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017) — 479 citations
• Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features (2014) — 334 citations
• Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer (2012) — 105 citations
• Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features (2008) — 331 citations