Researching medical image perception can improve the practice of radiology, pathology, and allied fields by understanding how experts perceive and interpret medical images, which can improve detection and diagnosis. This research explores questions such as:
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Why does a radiologist miss signs of cancer in a CT scan?
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What is the best way to train dermatologists to recognize cancerous skin lesions?
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How do time pressures affect performance in mammography?
This resource supports medical image perception research by helping investigators share their studies and recruit participants.
Use the table below to find studies that are recruiting participants, or submit your own study to add to this page.
For questions, contact Todd Horowitz.
These experiments are not conducted by the National Cancer Institute (NCI). NCI features studies on this page as a resource to the extramural community. The listing of any study, PI, or organization does not imply endorsement in the study’s validity or any research entity. Only medical image perception studies sponsored by a university or research center will be featured; commercial entities will not be included.
Submit Medical Image Perception Experiments
Improve the reach of your study’s recruitment by submitting your experiment for consideration to be featured here.
Submit a StudyStudies must meet the following criteria:
- Study is sponsored by a university or research center, and has institute and principal investigator (PI) approval to be featured on this page
- Study has IRB approval
- Study must relate to medical image perception
Ongoing Experiments
| Study | Principal Investigator | Participant Qualifications |
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Role of Cognitive Factors in Medical Image Perception Help further our understanding of how medical professionals interpret medical images. During this study, you will be shown mammograms and asked a series of questions about them. |
Dr. Jay Hegdé
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High-Throughput Truthing of Whole Slide Images to Validate Artificial Intelligence Algorithms We are crowdsourcing pathologists to collect annotations of the density of tumor-infiltrating lymphocytes in breast cancer to create a validation dataset for evaluating algorithm performance. |
Brandon Gallas
Katherine Elfer
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In this 30 minute study, you will see visual images and text-based scenarios related to your role. Part 1 is 5 short tasks and in Part 2 you will view 20 musculoskeletal X-Rays and asked to make a diagnosis. |
Ann Carrigan
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All Radiologists Make This Mistake - Prove Us Wrong Both radiologists and untrained observers have been shown to make systematic errors in the way they recall x-rays. Think you are different? Prove us wrong by doing this simple task. |
David Whitney |
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Brain AI study - Assessing the Clinical Utility of AI and its Explanation In this 30-min online survey, you will read 25 MRIs for patients with gliomas, and give your judgment on the tumor grading without and with AI assistance. You will receive a $50 Amazon gift card as a token of our appreciation. |
Dr. Ghassan Hamarneh
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