Human Perception of Time Intensity Curves from Dynamic Contrast-Enhanced Images
MD Anderson
We are seeking a motivated research intern to work in Dr. Garg's lab (garglab), in the Department of Abdominal Imaging at MD Anderson Cancer Center. This position will conduct research to improve the practice of radiology with applications in cancer imaging. We are interested in computer applications to radiology, particularly using deep learning.
The project(s) involve analysis of CT, MRI, PET scan data from cancer patients to automate detection and quantification of tumor burden in cancer patients. We have hand labeled data in the form of DICOM grayscale presentation states for various cancer types (colorectal, lymphoma, breast cancers), with metastases to various organs including liver, bone & peritoneum. We work on learning from this data and further refining / curating the data using trained AI models.
LEARNING OBJECTIVES
1. The trainee will learn how to use fully convolutional deep learning models for segmenting CT, MRI & PET scans.
2. The trainee will learn how to develop and optimize a data science pipeline: modeling, labeling user interface, data augmentation, and tumor simulation.
3. The trainee will learn how to apply novel deep learning models to oncological imaging applications and publish original research in peer reviewed journals.
ELIGIBILITY REQUIREMENTS
The ideal candidate must hold a bachelor's or master's degree; Degree must have been obtained recently (within one year) and be interested in attending medical school. Research experience is helpful, but not necessary. Applicants associated with a home institution do not qualify. Applicants in a clinical position, postdoctoral fellowship, or faculty position do not qualify. Research Interns cannot change titles to another trainee title upon completion (exception may apply).
ADDITIONAL APPLICATION INFORMATION
Expertise or skills in the following areas are highly desirable: PyTorch, scikit-image, scikit-learn, Sql, linux, pydicom. We are looking for an outstanding researcher with high motivation, ability to work collaboratively and independently, and a strong commitment to working on the above project.
POSITION INFORMATION
05/31/2025 Apply
The project(s) involve analysis of CT, MRI, PET scan data from cancer patients to automate detection and quantification of tumor burden in cancer patients. We have hand labeled data in the form of DICOM grayscale presentation states for various cancer types (colorectal, lymphoma, breast cancers), with metastases to various organs including liver, bone & peritoneum. We work on learning from this data and further refining / curating the data using trained AI models.
LEARNING OBJECTIVES
1. The trainee will learn how to use fully convolutional deep learning models for segmenting CT, MRI & PET scans.
2. The trainee will learn how to develop and optimize a data science pipeline: modeling, labeling user interface, data augmentation, and tumor simulation.
3. The trainee will learn how to apply novel deep learning models to oncological imaging applications and publish original research in peer reviewed journals.
ELIGIBILITY REQUIREMENTS
The ideal candidate must hold a bachelor's or master's degree; Degree must have been obtained recently (within one year) and be interested in attending medical school. Research experience is helpful, but not necessary. Applicants associated with a home institution do not qualify. Applicants in a clinical position, postdoctoral fellowship, or faculty position do not qualify. Research Interns cannot change titles to another trainee title upon completion (exception may apply).
ADDITIONAL APPLICATION INFORMATION
Expertise or skills in the following areas are highly desirable: PyTorch, scikit-image, scikit-learn, Sql, linux, pydicom. We are looking for an outstanding researcher with high motivation, ability to work collaboratively and independently, and a strong commitment to working on the above project.
POSITION INFORMATION
05/31/2025 Apply
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