Allan Halpern
Artificial Intelligence · 2003–2020
A researcher in the Science corpus: 10 papers; trusts 5 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 49 citations into it; #30,064 of 35,345 by trust here, #5,221 by citations
- Oncology — 46 citations into it; #909 of 1,698 by trust here, #302 by citations
- Computer Vision and Pattern Recognition — 33 citations into it
Whom Allan Halpern trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Allan Halpern overall
Papers from Allan Halpern's perspective
Ordered by how strongly the researchers Allan Halpern trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- Fully convolutional networks for semantic segmentation
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
- FaceNet: A unified embedding for face recognition and clustering
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Face Recognition
Their papers
- Human–computer collaboration for skin cancer recognition (2020) — 912 citations
- A patient-centric dataset of images and metadata for identifying melanomas using clinical context (2020) — 562 citations
- Deep learning ensembles for melanoma recognition in dermoscopy images (2016) — 600 citations
- Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) (2019) — 1,802 citations
- Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study (2019) — 455 citations
- BCN20000: Dermoscopic Lesions in the Wild (2019) — 653 citations
- Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images (2018) — 45 citations
- Results of the 2016 International Skin Imaging Collaboration ISBI Challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images (2017) — 294 citations
- Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) (2016) — 2,727 citations
- Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. (2003) — 1,204 citations