Madabhushi, Anant

Applications of machine learning in drug discovery and development

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 3,228

Histopathological Image Analysis: A Review

Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 2,052

Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology

Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 1,698

Image analysis and machine learning in digital pathology: challenges and opportunities

Madabhushi, A., & Lee, G. (2016) · Medical Image Analysis

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 1,093

Evaluation of prostate segmentation algorithms for mri: The promise12 challenge

Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 857

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., & Madabhushi, A. (2016) · IEEE Transactions on Medical Imaging

AI in cancer detectionCell Image Analysis Techniques

Cited by 854

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology

Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

Cited by 819

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update

Tejani, A. S., Klontzas, M. E., Gatti, A. A., Mongan, J. T., Moy, L., Park, S. H., Kahn, C. E., Panel, F. T. C. 2. U., Abbara, S., Afat, S., et al. (2024) · Radiology: Artificial Intelligence

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 570

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 403

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer

Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P. D., Thawani, R., Corredor, G., Alilou, M., Bera, K., Fu, P., et al. (2020) · Cancer Immunology Research

Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

Cited by 297

Automated grading of breast cancer histopathology using spectral clusteringwith textural and architectural image features

Doyle, S., Agner, S., Madabhushi, A., Feldman, M., & Tomaszewski, J. (2008) · 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 276

Digital pathology and computational image analysis in nephropathology

Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A., & Balis, U. G. J. (2020) · Nature Reviews Nephrology

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 264

Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma

Beig, N., Patel, J., Prasanna, P., Hill, V., Gupta, A., Correa, R., Bera, K., Singh, S., Partovi, S., Varadan, V., et al. (2018) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 157

Quality control stress test for deep learning-based diagnostic model in digital pathology

Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., et al. (2021) · Modern Pathology

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 146

CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction

Vaidya, P., Bera, K., Gupta, A., Wang, X., Corredor, G., Fu, P., Beig, N., Prasanna, P., Patil, P. D., Velu, P. D., et al. (2020) · The Lancet Digital Health

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment

Cited by 134

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

Wang, X., Barrera, C., Bera, K., Viswanathan, V. S., Azarianpour-Esfahani, S., Koyuncu, C., Velu, P., Feldman, M. D., Yang, M., Fu, P., et al. (2022) · Science Advances

Cancer Immunotherapy and BiomarkersAI in cancer detection

Cited by 89

An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management

Martel, A. L., Hosseinzadeh, D., Senaras, C., Zhou, Y., Yazdanpanah, A., Shojaii, R., Patterson, E. S., Madabhushi, A., & Gurcan, M. N. (2017) · Cancer Research

AI in cancer detectionCell Image Analysis Techniques

Cited by 63

Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces

Sridhar, A., Doyle, S., & Madabhushi, A. (2015) · Journal of Pathology Informatics

Image Retrieval and Classification TechniquesAI in cancer detection

Cited by 34