A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth *

@article{Sankarapandian2021APD,
  title={A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth *},
  author={Sivaramakrishnan Sankarapandian and Saul A. Kohn and Vaughn Spurrier and Sean Grullon and R. Soans and Kameswari Devi Ayyagari and Ramachandra Vikas Chamarthi and Kiran Motaparthi and Jason B Lee and Wonwoo Shon and Michael J. Bonham and Julianna D. Ianni},
  journal={2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
  year={2021},
  pages={629-638}
}
Although melanoma occurs more rarely than several other skin cancers, patients’ long term survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high discordance rate among pathologists when distinguishing between melanoma and benign melanocytic lesions. A tool that allows pathology labs to sort and prioritize melanoma cases in their workflow could improve turnaround time by prioritizing challenging cases and routing them directly to the appropriate sub… Expand

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