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Research Areas
Biography and Research Information
OverviewAI-generated summary
Jacob Brecheisen's research focuses on the development and application of artificial intelligence (AI) systems for medical image analysis, specifically in the interpretation of chest X-rays (CXRs). His work aims to create AI systems that are not only accurate in diagnosis but also interpretable and controllable, modeling the cognitive processes of radiologists. This approach seeks to enhance diagnostic precision by decoding radiologists' focused attention during image interpretation.
Brecheisen has published three papers in this area, with his most recent work in 2024. His research has garnered 17 citations and he holds an h-index of 2. He collaborates with Trong Thang Pham at the University of Arkansas at Fayetteville, with whom he has co-authored two shared publications. Brecheisen's work is characterized by its interdisciplinary nature, combining expertise in AI with the practical demands of clinical radiology.
Metrics
- h-index: 2
- Publications: 3
- Citations: 18
Selected Publications
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ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions (2024)
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I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses (2024)
Collaboration Network
Top Collaborators
Trong Thang Pham
University of Arkansas at Fayetteville
2 shared publications
In database
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses
Ngan Le
University of Arkansas at Fayetteville (US)
2 shared publications
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses
Anh Tuan Nguyen
1 shared publication
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses
Hien Nguyen
1 shared publication
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses
Anh Phong Nguyen
University of Liverpool (GB)
1 shared publication
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
Hien Nguyen
University of Houston (US)
1 shared publication
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
Trong-Thang Pham
University of Arkansas at Fayetteville (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Carol C. Wu
The University of Texas MD Anderson Cancer Center (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Hien Van Nguyen
University of Houston (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Zhigang Deng
University of Houston (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Donald Adjeroh
Donaldson (United States) (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Gianfranco Doretto
West Virginia University Hospitals (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Arabinda K. Choudhary
University of Arkansas for Medical Sciences (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Ngan Le
University of Arkansas at Little Rock (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
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