Jun Deng, PhD

Professor of Therapeutic Radiology

Research Interests

Environment and Public Health; Radiation, Ionizing; Radiation Oncology; Radiotherapy, Image-Guided

Public Health Interests

Cancer; Cancer screening

Research Organizations

Therapeutic Radiology: Deng Lab | Radiological Physics

Faculty Research

Yale Cancer Center: Radiobiology & Radiotherapy

Office of Cooperative Research

Research Summary

Dr. Deng’s research has been focused on big data, machine learning, artificial intelligence, and medical imaging for early cancer risk prediction, detection and prevention. In 2013 his group developed CT Gently, the world’s first iPhone App that can be used to estimate organ doses and associated cancer risks from CT and CBCT scans. Recently, Dr. Deng has been funded by a NIH R01 grant to develop a personal organ dose archive (PODA), a big data approach to developing an early warning system by actively tracking all organ dose data for individual patients in order to improve patient safety in radiation therapy.

Specialized Terms: Big Data; Machine Learning; Artificial Intelligence; Medical Imaging; Personalized Imaging Protocols; Cancer Risk; Organ Doses; Image-Guided Radiation Therapy (IGRT);

Extensive Research Description

Dr. Deng's research has been focused on big data, machine learning, artificial intelligence, and medical imaging for early cancer risk prediction, detection and prevention. Some active projects are listed below:

  1. Tracking organ doses for patient safety in radiation therapy;
  2. Machine learning with radiation oncology big data;
  3. Natural language processing for data mining and clinical decision support;
  4. Individualized cancer risk management;

Selected Publications

Full List of PubMed Publications

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Contact Info

Jun Deng, PhD
Office Location
Smilow Cancer Hospital at Yale New Haven
35 Park Street, Ste LL 508

New Haven, CT 06511
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Mailing Address
PO Box 208040
New Haven, CT 06520-8040

Jun Deng Team

Machine Learning with Radiation Oncology Big data

A Research Topic dedicated to machine learning algorithms for big data in radiation oncology hosted in Frontiers in Oncology. http://journal.frontiersin.org/researchtopic/6126/machine-learning-with-radiation-oncology-big-data