Mahdieh Kazemimoghadam, Ph.D.

Mahdieh Kazemimoghadam, Ph.D., is an Instructor at UT Southwestern in the Division of Medical Physics and Bioengineering specializing in machine learning, deep learning, and artificial intelligence-based algorithms to improve patient care and to accommodate automating clinical workflow.

Dr. Kazemimoghadam earned her doctorate degree from UT Dallas. Following graduation, she started a postdoctoral research position at UT Southwestern in the Medical Artificial Intelligence and Automation Lab (MAIA). Her research interests include medical image segmentation, deep learning in healthcare, and integrating artificial intelligence into clinical workflow.

• Artificial intelligence in medicine
• Deep learning and predictive models
• Medical image segmentation

Leveraging global binary masks for structure segmentation in medical images

Kazemimoghadam M, Yang Z, Chen M, Ma L, Lu W, Gu X Physics in medicine and biology 2023 Sep 68

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI)

Kazemimoghadam M, Yang Z, Chen M, Rahimi A, Kim N, Alluri P, Nwachukwu C, Lu W, Gu X Physics in medicine and biology 2023 May 68

Ensemble learning for glioma patients overall survival prediction using pre-operative MRIs

Yang Z, Chen M, Kazemimoghadam M, Ma L, Stojadinovic S, Wardak Z, Timmerman R, Dan T, Lu W, Gu X Physics in medicine and biology 2022 Dec 67

An Activity Recognition Framework for Continuous Monitoring of Non‐Steady‐State Locomotion of Individuals with Parkinson’s Disease

Kazemimoghadam M, Fey NP Applied Sciences (Switzerland) 2022 May 12

Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation

Yang Z, Chen M, Kazemimoghadam M, Ma L, Stojadinovic S, Timmerman R, Dan T, Wardak Z, Lu W, Gu X Physics in medicine and biology 2022 Jan 67

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation

Kazemimoghadam M, Chi W, Rahimi A, Kim N, Alluri P, Nwachukwu C, Lu W, Gu X Physics in medicine and biology 2021 Sep 66

Continuous Classification of Locomotion in Response to Task Complexity and Anticipatory State

Kazemimoghadam M, Fey NP Frontiers in Bioengineering and Biotechnology 2021 Apr 9

Biomechanical signals of varied modality and location contribute differently to recognition of transient locomotion

Kazemimoghadam M, Fey NP Sensors (Switzerland) 2020 Sep 20 1-13

Body Segment Mechanical Signal Contributions to Continuous Prediction of Locomotor Transitions Performed under Varying Anticipation

Kazemimoghadam M, Fey NP 2019 Jul 5331-5334

Continuous Classification of Locomotor Transitions Performed under Altered Cutting Style, Complexity and Anticipation

Kazemimoghadam M, Li W, Fey N 2018 Oct 972-977

• American Association of Physicists in Medicine (2020)