Anant Madabhushi

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Robert Woodruff Professor

Emory University

United States

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Dr. Anant Madabhushi is the Robert Woodruff Professor at Emory University in the United States. His research focuses on the application of artificial intelligence in oncology, radiology, and pathology, with recent articles discussing innovations in tumor-infiltrating lymphocyte analysis, HIV diagnosis using X-ray imaging, and predictive modeling in cancer outcomes. He emphasizes the importance of computational morphology and radiomics in personalizing treatment strategies across diverse populations.

Recent Grants

Grant: Close

MR Fingerprinting and Computerized Decision Support for Prostate Cancer

Open Date: 2017-03-02

Close Date: 2022-02-28

Grant: Close

Computerized histologic image predictor of cancer outcome

Open Date: 2016-07-01

Close Date: 2021-06-30

Grant: Close

Pathology Image Informatics Platform for visualization, analysis and management

Open Date: 2015-09-17

Close Date: 2020-08-31

Grant: Close

Computerized morphologic-molecular predictor of progression in DCIS

Open Date: 2015-04-01

Close Date: 2018-03-31

Grant: Close

Histologic image-based aggressiveness prediction in p16+ oropharyngeal carcinoma

Open Date: 2014-09-08

Close Date: 2017-08-31

Anant Madabhushi

Research Interests

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Recent Grants

Articles (28)

Computational Morphological Assessment of Bladder Cancer Tissue Is Prognostic of Recurrence and Overall Survival Following Transurethral Resection

Artificial intelligence defines spatial patterns of tumor-infiltrating lymphocytes highly associated with outcome – a pan-GI cancer study

Opportunistic use of artificial intelligence with X-ray imaging for diagnosis of HIV status in tuberculosis patients in Uganda and Tanzania

AI-based radiomic features predict outcomes and the added benefit of chemoimmunotherapy over chemotherapy in extensive stage small cell lung cancer: A multi-institutional study

Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients

Fundus Photograph-Derived Computational Features Predict Risk of Cardiovascular Events in the Chronic Renal Insufficiency Cohort Clinical Observational Study

Population-Specific Radiomics From Biparametric Magnetic Resonance Imaging Improves Prostate Cancer Risk Stratification in African American Men

Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer

Artificial intelligence in digital pathology — time for a reality check

When multiple instance learning meets foundation models: Advancing histological whole slide image analysis

Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment

Failing to Make the Grade: Conventional Cardiac Allograft Rejection Grading Criteria Are Inadequate for Predicting Rejection Severity

Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study

Towards equitable AI in oncology

Image analysis Uncovers associations between immune landscape, collagen structure, and neoadjuvant chemotherapy in high-grade serous ovarian carcinomas

PatchSorter: a high throughput deep learning digital pathology tool for object labeling

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

Automatic myeloblast segmentation in acute myeloid leukemia images based on adversarial feature learning

Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancer

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer

Novel Fractal-based Sub-RPE Compartment OCT Radiomics Biomarkers are Associated with Subfoveal Geographic Atrophy in Dry AMD

Vision Transformers for Computational Histopathology

Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study

Oropharyngeal cancer outcomes correlate with p16 status, multinucleation and immune infiltration.

Integrating Radiomics With Clinicoradiological Scoring Can Predict High-Risk Patients Who Need Surgery in Crohn's Disease: A Pilot Study.

Computational Imaging Biomarker Correlation with Intraocular Cytokine Expression in Diabetic Macular Edema

Collaborators (22)

Mohammadhadi Khorrami

Kenneth Margulies

Satish Viswanath

Mahmut Akgul

Rainer Grobholz

Leonardo Kayat Bittencourt

Malak Abedalthagafi

Alberto J. Montero

Holly Harper

Kaustav Bera

Maneesh Dave

Wendong Yu

Pingfu Fu

Behtash Ghazi Nezami

Sunil Badve

Eliot Peyster

Jeonghyun Kang

Yijiang Chen

Patricia Castro

Virginia Hill

Pallavi Tiwari

Jonathan T. C. Liu

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