Maryellen Lissak Giger

Radiology, Nuclear Medicine and Imaging · 2001–2019

A researcher in the Science corpus: 10 papers; trusts 80 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Radiology, Nuclear Medicine and Imaging — 118 citations into it; #378 of 3,265 by trust here, #480 by citations
• Artificial Intelligence — 116 citations into it; #9,101 of 35,345 by trust here, #7,606 by citations
• Computer Vision and Pattern Recognition — 102 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Radiation — 9 citations into it

Whom Maryellen Lissak Giger trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Yann LeCun
6. Benjamin Q. Huynh
7. Kaiming He
8. Jason Yosinski
9. Diederik P. Kingma
10. Karen Simonyan

Who trusts Maryellen Lissak Giger overall

1. Meghavi Rana
2. Carrie J. Cai
3. Berkman Sahiner
4. Megha Bhushan
5. Arvid Lundervold
6. Samantha Winter
7. Aria Pezeshk
8. Lubomir M. Hadjiiski
9. David F. Steiner
10. Shengfeng Liu

Papers from Maryellen Lissak Giger's perspective

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

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

Their papers

• Machine Learning in Medical Imaging. (2018) — 238 citations
• Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019) — 1,608 citations
• Deep learning in medical imaging and radiation therapy. (2018) — 668 citations
• A Deep Feature Fusion Methodology for Breast Cancer Diagnosis Demonstrated on Three Imaging Modality Datasets (2017) — 358 citations
• Digital mammographic tumor classification using transfer learning from deep convolutional neural networks (2016) — 486 citations
• Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study. (2011) — 26 citations
• Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. (2008) — 361 citations
• A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1 (2006) — 310 citations
• Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. (2002) — 306 citations
• Automated detection of lung nodules in CT scans: preliminary results. (2001) — 298 citations