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Khalid Niazi, PhD, MS

Associate Professor

Portrait of Khalid Niazi, PhD

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Email Dr. Niazi

Mailing Address:

Pelotonia Research Center
Office # 2043
2255 Kenny Rd
Columbus, OH 43210

Biosketch

I am an Associate Professor at The Ohio State University, specializing in computational pathology with a focus on integrating artificial intelligence (AI) into pathology to enhance disease detection, diagnosis, and prognosis. My work leverages advanced machine learning techniques, including deep learning, to analyze complex histopathological images, aiming to improve accuracy and efficiency in cancer detection and other diagnostic applications. With over a decade of experience in biomedical informatics and pathology, I am dedicated to bridging the gap between computational science and clinical practice to support precision medicine and global health equity.

Research Focus

My research interests lie at the intersection of AI and pathology, where I develop tools to transform digital pathology workflows. I focus on creating explainable AI models that assist pathologists by identifying key cellular features, detecting disease patterns, and predicting patient outcomes. I am particularly committed to making these innovations accessible to resource-limited settings, where diagnostic capabilities are often restricted.

Key Contributions

  • AI-Enhanced Cancer Detection: I have led projects developing AI models that analyze pathology slides to identify cancer markers with high precision, supporting pathologists in improving diagnostic accuracy and speed.
  • Explainable AI in Pathology: My work emphasizes interpretable AI, allowing clinicians to understand and trust AI-driven insights. This approach facilitates the safe integration of AI tools into clinical workflows.
  • Global Health and Accessibility: I am involved in projects adapting AI models for low-resource environments, working to make diagnostic tools accessible worldwide, especially in underserved areas.

Opportunities for Students and Trainees

I welcome inquiries from prospective PhD students interested in pursuing a career in computational pathology. As an affiliated faculty member of the Biomedical Engineering (BME) Department, I offer students the opportunity to earn a PhD in BME with a specialization in computational pathology. Additionally, I encourage undergraduate students interested in digital or computational pathology to reach out regarding research opportunities.

Medical students, residents and postdoctoral fellows who are passionate about computational pathology and aim to build a career in this field are also invited to contact me. I am committed to mentoring the next generation of researchers in this transformative field.

Vision

I envision a future where AI seamlessly supports pathologists, transforming diagnostics and enabling personalized treatment decisions. By pioneering AI tools that are both powerful and interpretable, my goal is to enhance healthcare delivery and ensure equitable access to advanced diagnostic technology worldwide.

Selected Grants

  • NIH R01 Grant on Tumor Bud Detection in Colorectal Cancer: Principal Investigator on a project developing deep learning models for tumor bud detection and risk stratification in colorectal cancer.
  • NIBIB Trailblazer Award (Three year R21): Leading the development of tools to predict difficult airway cases, enhancing safety and minimizing complications in clinical settings.
  • Co-Investorgator: I am also a Co-Investigator on two R01s and a R21.
  • DoD Project on Multi-Organoid Systems: As an AI expert, I contributed to a project using multi-organoid data and machine learning to forecast disease and toxicity outcomes.

Academic and Medical Appointments

2024-Present Associate Professor of Pathology, Department of Pathology, The Ohio State University, Columbus, OH

Education and Training

2012-2014 Postdoctoral Training in Pathology Informatics, The Ohio State University, Columbus, OH

2011 PhD, Medical Image Analysis, Uppsala University, Sweden

Selected Publications

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  • Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool Z Su, Y Guo, R Wesolowski, G Tozbikian, NS O'Connell, M Niazi, ... arXiv preprint arXiv:2409.15491 2024
  • Deep Learning Model for Predicting Lung Adenocarcinoma Recurrence from Whole Slide Images Z Su, U Afzaal, S Niu, MM de Toro, F Xing, J Ruiz, MN Gurcan, W Li, ... Cancers 16 (17), 3097 2024
  • Machine learning-based analysis of Ebola virus' impact on gene expression in nonhuman primates M Rezapour, MKK Niazi, H Lu, A Narayanan, MN Gurcan Frontiers in Artificial Intelligence 7, 1405332 2024
  • Gene pointNet for tumor classification H Lu, M Rezapour, H Baha, MKK Niazi, A Narayanan, MN Gurcan Neural Computing and Applications, 1-15 2024
  • IASLC grading system predicts distant metastases for resected lung adenocarcinoma Y Wang, MR Smith, CB Dixon, R D'Agostino, Y Liu, J Ruiz, MD Chan, J Su, ... Journal of Clinical Pathology 2024
  • Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images Z Su, M Rezapour, U Sajjad, S Niu, MN Gurcan, MKK Niazi IEEE Journal of Biomedical and Health Informatics 2024
  • B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung … D Koyuncu, T Tavolara, DM Gatti, AC Gower, ML Ginese, I Kramnik, ... Infection and Immunity 92 (7), e00263-23 2024
  • Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice DM Gatti, AL Tyler, JM Mahoney, GA Churchill, B Yener, D Koyuncu, ... PLOS Pathogens 20 (6), e1011915 2024
  • Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging JMK Davis, MKK Niazi, AB Ricker, TE Tavolara, JN Robinson, ... Journal of surgical oncology 2024
  • Tympanic membrane segmentation of video frames to create composite images using SAM S Camalan, MKK Niazi, C Elmaraghy, AC Moberly, MN Gurcan Medical Imaging 2024: Computer-Aided Diagnosis 12927, 766-773 2024
  • Few-shot tumor bud segmentation using generative model in colorectal carcinoma Z Su, W Chen, PJ Leigh, U Sajjad, S Niu, M Rezapour, WL Frankel, ... Medical Imaging 2024: Digital and Computational Pathology 12933, 51-57 2024
  • Combining frontal and profile view facial images to predict difficult-to-intubate patients using AI Z Su, TE Tavolara, U Sajjad, MN Gurcan, S Segal, MKK Niazi Medical Imaging 2024: Computer-Aided Diagnosis 12927, 125-131 2024
  • Enhancing colorectal cancer tumor bud detection using deep learning from routine H&E-stained slides U Sajjad, W Chen, M Rezapour, Z Su, T Tavolara, WL Frankel, ... Medical Imaging 2024: Digital and Computational Pathology 12933, 199-205 2024
  • Deep-ODX: an efficient deep learning tool to risk stratify breast cancer patients from histopathology images Z Su, A Rosen, R Wesolowski, G Tozbikian, MKK Niazi, MN Gurcan Medical Imaging 2024: Digital and Computational Pathology 12933, 34-39 2024
  • Adapting SAM to histopathology images for tumor bud segmentation in colorectal cancer Z Su, W Chen, S Annem, U Sajjad, M Rezapour, WL Frankel, MN Gurcan, ... Medical Imaging 2024: Digital and Computational Pathology 12933, 64-69 2024
  • Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning TE Tavolara, MKK Niazi, AL Feldman, DL Jaye, C Flowers, LAD Cooper, ... Diagnostic Pathology 19 (1), 17 2024
  • A Comparative Analysis of RNA-Seq and NanoString Technologies in Deciphering Viral Infection Response in Upper Airway Lung Organoids M Rezapour, MKK Niazi, S Walker, DA Ornelles, PM McNutt, A Atala, ... Frontiers in Genetics 15, 1327984 2024
  • Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images Z Su, M Rezapour, U Sajjad, MN Gurcan, MKK Niazi Computers in Biology and Medicine 167, 107607 2023
  • One label is all you need: Interpretable AI-enhanced histopathology for oncology TE Tavolara, Z Su, MN Gurcan, MKK Niazi Seminars in Cancer Biology 15 2023
  • The correlation between rheological properties and extrusion-based printability in bioink artifact quantification GJ Gillispie, J Copus, M Uzun-Per, JJ Yoo, A Atala, MKK Niazi, SJ Lee Materials & Design 233, 112237 2023