Publications

  • GenBlosum: On Determining Whether Cancer Mutations Are Functional or Random
    Alejandro Leyva, M. Khalid Khan Niazi
    (Published in Genes (MDPI); doi: 10.3390/genes17010055)
  • Quantum Chemical Profiling of Protein Mutations via Fragment-Based DFT
    Alejandro Leyva, M. Khalid Khan Niazi
    (under review)
  • Predicting Methylation-Based Replication Timing from Whole Slide Images
    Alejandro Leyva, Abdul Rehman Akbar, M. Khalid Khan Niazi
    (under review)
  • Integrating Image and Text-Based AI Improves Identification of Metastatic Sites from Whole-Slide Pathology Images
    Yixin Chen, Ziyu Su, Muhammad Khalid Niazi, Anil Vasdev Parwani, Elshad Hasanov
    Accepted for presentation AACR 2026
  • Low-Magnification Deep Learning Model for Rapid HER2 Status Prediction from H&E Whole-Slide Images
    Ziyu Su, Abdul Rehman Akbar, Usama Sajjad, Sansar Babu Tiwari, Elshad Hassanov, Arya Mariam Roy, Zaibo Li, Daniel Stover, Muhammad Khalid Khan Niazi
    Accepted for presentation AACR 2026
  • Deploying Artificial Intelligence Driven Digital Pathology for Real World Clinical Decision-Making in Pancreatic Cancer
    Ashish Manne, Abdul Rehman Akbar, Alejandro Levya, Upender Manne, Anne Noonan, Anup Kasi, Ashwini Esnakula, Ravi Kumar Paluri, Anil Parwani, Muhammad Khalid Khan Niazi
    Accepted for presentation AACR 2026
  • AI-Driven Subtyping in Pancreatic Ductal Adenocarcinoma Using H&E Whole Slide Images
    Alejandro Leyva, Abdul Rehman Akbar, Anil V. Parwani, Wei Chen, Ashish Manne, Muhammad Khalid Khan Niazi
    Accepted for presentation, USCAP 2026
  • An AI Pathology Assistant for Clinical Deployment: Evidence-Based Case Retrieval, Outcome Insights, and Automated Reporting
    Abdul Rehman Akbar, Samuel Wales-McGrath, Usman Afzaal, Ziyu Su, Wei Chen, Anil V. Parwani, Muhammad Khalid Khan Niazi
    Accepted for presentation, USCAP 2026
  • Bridging Histology and Genomics: Artificial Intelligence (AI) Predicts Colorectal Cancer Genetic Mutations from H&E Images
    Abdul Rehman Akbar, Usama Sajjad, Ashish Manne, Wendy L. Frankel, Zaibo Li, Ning Jin, Anil V. Parwani, Wei Chen, Muhammad Khalid Khan Niazi
    Accepted for presentation, USCAP 2026
  • An AI Virtual Agent for Prognostic Interpretation: Translating Black-Box Predictions into Clinical Insights
    Usama Sajjad, Abdul Rehman Akbar, Hikmat Khan, Wendy L. Frankel, Metin N. Gurcan, Anil V. Parwani, Wei Chen, Muhammad Khalid Khan Niazi
    Accepted for presentation, USCAP 2026
  • AI-Based Detection of Megakaryocyte Dysplasia in Myelodysplastic Neoplasms
    Wenfang Liu, Hikmat Khan, Adrian Rajab, Yulan Jin, Muhammad Khalid Khan Niazi
    Accepted for presentation, USCAP 2026
  • Hyperparameter Optimization and Reproducibility in Deep Learning Model Training
    Usman Afzaal, Ziyu Su, Usama Sajjad, Hao Lu, Mostafa Rezapour, Metin Nafi Gurcan, Muhammad Khalid Khan Niazi
    (under review)
  • Progressive Translation of H&E to IHC with Enhanced Structural Fidelity
    Yuhang Kang, Ziyu Su, Tianyang Wang, Zaibo Li, Wei Chen, Muhammad Khalid Khan Niazi
    (under review)
  • Morphology-Aware Prognostic Model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Images
    Usama Sajjad, Abdul Rehman Akbar, Ziyu Su, Deborah Knight, Wendy L Frankel, Metin N Gurcan, Wei Chen, Muhammad Khalid Khan Niazi
    (under review)
  • Streamline Pathology Foundation Model by Cross-Magnification Distillation
    Ziyu Su, Abdul Rehman Akbar, Usama Sajjad, Anil V Parwani, Muhammad Khalid Khan Niazi
    (under review)
  • CellEcoNet: Decoding the Cellular Language of Pathology with Deep Learning for Invasive Lung Adenocarcinoma Recurrence Prediction
    Abdul Rehman Akbar, Usama Sajjad, Ziyu Su, Wencheng Li, Fei Xing, Jimmy Ruiz, Wei Chen, Muhammad Khalid Khan Niazi
    (under review)
  • Adapting Image Foundational Model to Identify Tumor Budding from H&E Images in Colorectal Cancer Diagnostics
    Z Su, U Sajjad, WL Frankel, MN Gurcan, W Chen, MKK Niazi
    Cancer Detection and Diagnosis: A Handbook of Emerging Technologies, 110
  • Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images
    H Khan, Z Su, H Zhang, Y Wang, B Ning, S Wei, H Guo, Z Li, MKK Niazi
    Cancers 17 (15), 2423
  • 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, MKK Niazi
    Modern Pathology, 100847
  • 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, MKK Niazi
    Journal of Clinical Pathology 78 (6), 409-415
  • HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data
    U Afzaal, Z Su, U Sajjad, T Stack, H Lu, S Niu, AR Akbar, MN Gurcan, MKK Niazi
    Patterns
  • Artificial intelligence in breast pathology: overview and recent updates
    S Datwani, H Khan, MKK Niazi, AV Parwani, Z Li
    Human Pathology, 105819
  • Creating a tumor bud ground truth of h&e stained slides and automated identification of tumor buds using an h&e stained slides
    MN Gurcan, WL FRANKEL, W Chen, MKK Niazi
    US Patent App. 18/730,593
  • Clinically interpretable imaging biomarker discovery in BCG-vaccinated mycobacterium tuberculosis-infected diversity outbred mice using deep learning
    U Sajjad, MKK Niazi, G Beamer, MN Gurcan
    Medical Imaging 2025: Digital and Computational Pathology 13413, 152-158
  • Instruction tuning for colorectal histopathology: a multimodal vision-language assistant on human-evaluated data
    U Afzaal, Z Su, U Sajjad, T Stack, M Rezapour, H Lu, S Niu, MN Gurcan, MKK Niazi
    Medical Imaging 2025: Digital and Computational Pathology 13413, 127-134
  • Tumor Bud Classification in Colorectal Cancer Using Attention-Based Deep Multiple Instance Learning and Domain-Specific Foundation Models
    M Şeker, MKK Niazi, W Chen, WL Frankel, MN Gurcan
    Cancers 17 (7), 1245
  • Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation
    W Ma, I Oh, Y Luo, S Kumar, A Gupta, AM Lai, V Puri, D Kreisel, MKK Niazi
    American Journal of Transplantation
  • Classification-based pathway analysis using GPNet with novel P-value computation
    H Lu, M Rezapour, H Baha, M Khalid Khan Niazi, A Narayanan, MN Gurcan
    Briefings in Bioinformatics 26 (1), bbaf039
  • An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study
    A Capar, DA Ekinci, M Ertano, MKK Niazi, et al.
    PloS one 19 (12), e0314450
  • Exploring the host response in infected lung organoids using NanoString technology: A statistical analysis of gene expression data
    M Rezapour, SJ Walker, DA Ornelles, MKK Niazi, et al.
    PloS one 19 (11), e0308849
  • Gene pointNet for tumor classification
    H Lu, M Rezapour, H Baha, MKK Niazi, et al.
    Neural Computing and Applications 36 (33), 21107-21121
  • 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, MKK Niazi, et al.
    arXiv preprint arXiv:2409.15491
  • 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, MKK Niazi, et al.
    Cancers 16 (17), 3097
  • 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
  • IASLC grading system predicts distant metastases for resected lung adenocarcinoma
    Y Wang, MR Smith, CB Dixon, R D’Agostino, Y Liu, J Ruiz, MKK Niazi, et al.
    Journal of Clinical Pathology
  • 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
  • B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice
    D Koyuncu, T Tavolara, DM Gatti, AC Gower, ML Ginese, I Kramnik, et al.
    Infection and Immunity 92 (7), e00263-23
  • 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, et al.
    PLOS Pathogens 20 (6), e1011915
  • 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, et al.
    Journal of surgical oncology
  • 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
  • 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, et al.
    Medical Imaging 2024: Digital and Computational Pathology 12933, 51-57
  • 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
  • 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, et al.
    Medical Imaging 2024: Digital and Computational Pathology 12933, 199-205
  • 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
  • Adapting SAM to histopathology images for tumor bud segmentation in colorectal cancer
    Z Su, W Chen, S Annem, U Sajjad, M Rezapour, WL Frankel, MKK Niazi, et al.
    Medical Imaging 2024: Digital and Computational Pathology 12933, 64-69
  • 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, et al.
    Diagnostic Pathology 19 (1), 17
  • 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, et al.
    Frontiers in Genetics 15, 1327984
  • 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
  • One label is all you need: Interpretable AI-enhanced histopathology for oncology
    TE Tavolara, Z Su, MN Gurcan, MKK Niazi
    Seminars in Cancer Biology
  • 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
  • Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults
    PM Bunch, J Rigdon, MKK Niazi, RT Barnard, RD Boutin, DK Houston, et al.
    Academic Radiology
  • NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images
    U Sajjad, M Rezapour, Z Su, GH Tozbikian, MN Gurcan, MKK Niazi
    Cancers 15 (13), 3428
  • Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers
    M Rezapour, MKK Niazi, MN Gurcan
    Scientific Reports 13 (1), 6003
  • Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models
    Z Su, S Kumar, TE Tavolara, MN Gurcan, S Segal, MKK Niazi
    Medical Imaging 2023: Computer-Aided Diagnosis 12465, 644-649
  • Background detection affects downstream classification of Camelyon16 whole slide images
    TE Tavolara, MKK Niazi, MN Gurcan
    Medical Imaging 2023: Digital and Computational Pathology 12471, 164-169
  • Deep learning to predict the proportion of positive cells in CMYC-stained tissue microarrays of diffuse large B-cell lymphoma
    TE Tavolara, MKK Niazi, D Jaye, C Flowers, L Cooper, MN Gurcan
    Medical Imaging 2023: Digital and Computational Pathology 12471, 12-16
  • Simple patch-wise transformations serve as a mechanism for slide-level augmentation for multiple instance learning applications
    TE Tavolara, MKK Niazi, MN Gurcan
    Medical Imaging 2023: Digital and Computational Pathology 12471, 369-373
  • Minimizing the intra-pathologist disagreement for tumor bud detection on H&E images using weakly supervised learning
    TE Tavolara, W Chen, WL Frankel, MN Gurcan, MKK Niazi
    Medical Imaging 2023: Digital and Computational Pathology 12471, 277-283
  • The effects of sparsity induction methods on attention-based multiple instance learning applied to Camelyon16
    TE Tavolara, MN Gurcan, MKK Niazi
    Medical Imaging 2023: Digital and Computational Pathology 12471, 149-154
  • BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
    Z Su, MKK Niazi, TE Tavolara, S Niu, GH Tozbikian, R Wesolowski, et al.
    PloS one 18 (4), e0283562
  • Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases
    O Moaven, TE Tavolara, CD Valenzuela, CU Corvera, CH Cha, et al.
    Journal of the American College of Surgeons, 10.1097
  • Perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell gene expression pathways identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice
    D Koyuncu, T Tavolara, DM Gatti, AC Gower, M Ginese, I Kramnik, et al.
    bioRxiv, 2023.07. 27.550843
  • Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels
    TE Tavolara, MN Gurcan, MKK Niazi
    Cancers 14 (23), 5778
  • Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images
    Z Su, TE Tavolara, G Carreno-Galeano, SJ Lee, MN Gurcan, MKK Niazi
    Medical Image Analysis 79, 102462
  • Automatic generation of the ground truth for tumor budding using H&E stained slides
    TE Tavolara, A Dutta, MV Burks, W Chen, W Frankel, MN Gurcan, et al.
    Medical Imaging 2022: Digital and Computational Pathology 12039, 40-46
  • Predicting HER2 scores from registered HER2 and H&E images
    TE Tavolara, MKK Niazi, G Tozbikian, R Wesolowski, MN Gurcan
    Medical Imaging 2022: Digital and Computational Pathology 12039, 60-68
  • An extrusion-based bioink artifact for quantifying printability and an exploratory analysis on its relationships with rheological properties
    G Gillispie, M Uzun-Per, J Yoo, A Atala, MKK Niazi, SJ Lee
    TISSUE ENGINEERING PART A 28, S472-S472
  • Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring
    C Luchini, L Pantanowitz, V Adsay, SL Asa, P Antonini, I Girolami, et al.
    Modern Pathology, 1-9
  • Automated Tumor Budding Detection by Computer Algorithm that Uses Knowledge Learned from Cytokeratin Immunohistochemistry
    V Arole, T Tavolara, MKK Niazi, W Frankel, M Gurcan, D Knight, W Chen
    LABORATORY INVESTIGATION 102 (SUPPL 1), 1061-1062
  • OtoXNet-Automated Identification of Eardrum Diseases from Otoscope Videos: A Deep Learning Study for Video-representing Images
    H Binol, MKK Niazi, C Elmaraghy, AC Moberly, MN Gurcan
    Neural Computing and Applications
  • Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models
    TE Tavolara, MN Gurcan, S Segal, MKK Niazi
    Computers in Biology and Medicine 136, 104737
  • CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice
    D Koyuncu, MKK Niazi, T Tavolara, C Abeijon, ML Ginese, Y Liao, C Mark, et al.
    PLOS Pathogens 17 (8), e1009773
  • Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice
    TE Tavolara, M Niazi, A Gower, M Ginese, B Gillian, MN Gurcan
    Lancet EBioMedicine 67 (2021), 1-12
  • Computer Algorithm Learning Process and Initial Mistakes are Similar to Those Expected from Human Students in Evaluation of Colorectal Cancer Tumor Budding
    E Freitag, MKK Niazi, D Knight, M Gurcan, W Frankel, W Chen
    LABORATORY INVESTIGATION 101 (SUPPL 1), 411-412
  • Identifying lung imaging biomarkers of BCG vaccination after infection with Mycobacterium tuberculosis
    TE Tavolara, MKK Niazi, G Beamer, MN Gurcan
    Medical Imaging 2021: Digital Pathology 11603, 49-57
  • Grading and localization of histological features for bioengineered kidney constructs
    TE Tavolara, G Carreno-Galeano, MN Gurcan, SJ Lee, MKK Niazi
    Medical Imaging 2021: Digital Pathology 11603, 16-24
  • Panoptic segmentation of wounds in a pig model
    TE Tavolara, AM Jorgensen, MN Gurcan, SV Murphy, MKK Niazi
    Medical Imaging 2021: Computer-Aided Diagnosis 11597, 444-451
  • Automated video summarization and label assignment for otoscopy videos using deep learning and natural language processing
    H Binol, MKK Niazi, C Elmaraghy, AC Moberly, MN Gurcan
    Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications
  • Bladder cancer organoid image analysis: textured-based grading
    S Camalan, MKK Niazi, M Devarasetty, S Soker, MN Gurcan
    Medical Imaging 2021: Digital Pathology 11603, 8-15
  • System and method for automated prediction of difficult airway management using images
    BS Segal, MKK Niazi
    US Patent App. 16/944,789
  • Tuberculosis biomarkers discovered using Diversity Outbred mice
    D Koyuncu, MKK Niazi, T Tavolara, C Abeijon, M Ginese, Y Liao, C Mark, et al.
    medRxiv, 2021.01. 08.20249024
  • Automated Image Analysis Methodologies to Compute Bioink Printability
    M Uzun-Per, GJ Gillispie, T Erol Tavolara, JJ Yoo, A Atala, M Nafi Gurcan, et al.
    Advanced Engineering Materials
  • Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning
    TE Tavolara, MKK Niazi, M Ginese, C Piedra-Mora, DM Gatti, G Beamer, et al.
    EBioMedicine 62
  • The influence of printing parameters and cell density on bioink printing outcomes
    GJ Gillispie, A Han, M Uzun-Per, J Fisher, AG Mikos, MKK Niazi, JJ Yoo, et al.
    Tissue Engineering Part A 26 (23-24), 1349-1358
  • Semantic segmentation to identify bladder layers from H&E Images
    MKK Niazi, E Yazgan, TE Tavolara, W Li, CT Lee, A Parwani, MN Gurcan
    Diagnostic Pathology 15, 1-8
  • Digital otoscopy videos versus composite images: A reader study to compare the accuracy of ENT physicians
    H Binol, MKK Niazi, G Essig, J Shah, JK Mattingly, MS Harris, et al.
    The Laryngoscope (2020).
  • SelectStitch: Automated Frame Segmentation and Stitching to Create Composite Images from Otoscope Video Clips
    H Binol, AC Moberly, MKK Niazi, G Essig, J Shah, C Elmaraghy, T Teknos, et al.
    Applied Sciences, 10(17), 5894. 10 (17), 5894
  • History’s Mysteries Unlocked: Using animal population models and artificial intelligence to understand tuberculosis
    T Tavolara, MKK Niazi, T Westerling-Bui, M Gurcan, G Beamer
    The Pathologist Magazine
  • OtoMatch: Content-based eardrum image retrieval using deep learning
    C Seda, MKK Niazi, AC Moberly, T Teknos, G G, Essig, C Elmaraghy, et al.
    PLOS One 15 (5), e0232776
  • Segmentation of mycobacterium tuberculosis bacilli clusters from acid-fast stained lung biopsies: a deep learning approach
    TE Tavolara, MKK Niazi, G Beamer, MN Gurcan
    Medical Imaging 2020: Digital Pathology 11320, 92-98
  • Hotspot detection in pancreatic neuroendocrine images using local depth
    MKK Niazi, K Moore, KS Berenhaut, DJ Hartman, L Pantanowitz, et al.
    Medical Imaging 2020: Digital Pathology 11320, 41-46
  • Identifying bladder layers from H and E images using U-Net image segmentation
    MKK Niazi, E Yazgan, C Lee, A Parwani, MN Gurcan
    Medical Imaging 2020: Digital Pathology 11320, 27-34
  • A multidimensional scaling and sample clustering to obtain a representative subset of training data for transfer learning-based rosacea lesion identification
    H Binol, MKK Niazi, A Plotner, J Sopkovich, BH Kaffenberger, MN Gurcan
    Medical Imaging 2020: Computer-Aided Diagnosis 11314, 272-278
  • Author Correction: A modular cGAN classification framework: Application to colorectal tumor detection
    TE Tavolara, MKK Niazi, V Arole, W Chen, W Frankel, MN Gurcan
    Nature Scientific Reports 10 (1), 1-2
  • A modular cGAN classification framework: Application to colorectal tumor detection (vol 9, 18969, 2019)
    TE Tavolara, M Niazi, K Khan, V Arole, W Chen, W Frankel, MN Gurcan
    Nature Scientific Reports 10 (1)
  • Decision Fusion on Image Analysis and Tympanometry to Detect Eardrum Abnormalities
    H Binol, AC Moberly, MKK Niazi, G Essig, J Shah, C Elmaraghy, T Teknos, et al.
  • A modular cGAN classification framework: Application to colorectal tumor2019detection
    TE Tavolara, MKK Niazi, V Arole, W Chen, W Frankel, MN Gurcan
    Scientific reports 9 (1), 18969
  • Ros-NET: A deep convolutional neural network for automatic identification of rosacea lesions
    H Binol, A Plotner, J Sopkovich, B Kaffenberger, MKK Niazi, MN Gurcan
    Wiley Skin Research &Technology (https://doi.org/10.1111/srt.12817)
  • Digital pathology and artificial intelligence
    MKK Niazi, AV Parwani, MN Gurcan
    The Lancet Oncology 20 (5), e253-e261
  • Colorectal tumor identification by transferring knowledge from pan-cytokeratin to H&E
    TE Tavolara, MKK Niazi, W Chen, W Frankel, MN Gurcan
    Medical Imaging 2019: Digital Pathology 10956, 229-237
  • Segmentation of follicles from CD8-stained slides of follicular lymphoma using deep learning
    C Senaras, MKK Niazi, V Arole, W Chen, B Sahiner, A Shana’ah, et al.
    Medical Imaging 2019: Digital Pathology 10956, 159-165
  • Generalization of tumor identification algorithms
    MKK Niazi, TE Tavolara, C Senaras, G Tozbikian, DJ Hartman, V Arole, et al.
    Medical Imaging 2019: Digital Pathology 10956, 208-215
  • DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning
    C Senaras, MKK Niazi, G Lozanski, M Nafi Gurcan
    PLOS ONE
  • Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer
    MKK Niazi, Y Lin, F Liu, A Ashok, MW Marcellin, T G, MN Gurcan, A Bilgin
    Artificial Intelligence in Medicine
  • Relationship between the Ki67 index and its area based approximation in breast cancer
    MKK Niazi, C Senaras, M Pennell, V Arole, G Tozbikian, MN Gurcan
    BMC Cancer 18 (867)
  • Nuclear IHC enumeration: A digital phantom to evaluate the performance of automated algorithms in digital pathology
    MKK Niazi, FS Abas, C Senaras, M Pennell, B Sahiner, W Chen, J Opfer, et al.
    PLOS ONE, https://doi.org/10.1371/journal.pone.019
  • Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images
    C Senaras, MKK Niazi, B Sahiner, MP Pennell, G Tozbikian, G Lozanski, et al.
    PLOS ONE 13 (5), e0196846
  • Mp58-06 Automated staging of T1 bladder cancer using digital pathologic H&E images: a deep learning approach
    MKK Niazi, T Tavolara, V Arole, A Parwani, C Lee, M Gurcan
    The Journal of Urology 199 (4), e775
  • Automated T1 bladder risk stratification based on depth of lamina propria invasion from H and E tissue biopsies: a deep learning approach
    MKK Niazi, TE Tavolara, V Arole, AV Parwani, C Lee, MN Gurcan
    Medical Imaging 2018: Digital Pathology 10581, 134-142
  • Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning
    MKK Niazi, T Tavolara, V Arole, D Hartman, L Pantanowitz, MN Gurcan
    PLOS ONE
  • A Deep Learning Approach to Accurately Identify Different Layers of Bladder Wall Using Digital H &E Slides
    MKK Niazi, T Tavolara, V Arole, C Lee, AV Parwani, MN Gurcan
    USCAP, Nature Laboratory Investigation, supplement 1, 98, 595-595
  • A Novel Image Analysis Algorithm To Classify Bladder Wall Layers: A Step Towards Automated Sub-Staging Of T1 Bladder Cancer
    MKK Niazi, T Tavolara, V Arole, C Lee, AV Parwani, MN Gurcan
    USCAP, Nature Laboratory Investigation, Supplement 1, 98, 815
  • Identifying bladder layers from H and E tissue biopsies: a deep learning approach
    MKK Niazi, TE Tavolara, A Vidya, AV Parwani, CT Lee, MN Gurcan
    SPIE Medical Imaging 10581
  • An application of transfer learning to neutrophil cluster detection for tuberculosis: efficient implementation with nonmetric multidimensional scaling and sampling
    MKK Niazi, G Beamer, MN Gurcan
    SPIE Medical Imaging
  • Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer (vol 21, pg 1027, 2017)
    M Niazi, K Khan, K Yao, DL Zynger, SK Clinton, J Chen, M Koyuturk, et al.
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 21 (5), 1473-1474
  • Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer
    MKK Niazi, K Yao, D Zynger, S Clinton, J Chen, M Koyuturk, et al.
    IEEE Journal of Biomedical and Health Informatics 2 (4), 1027 – 1038
  • Computer-assisted quantification of CD3+ T cells in follicular lymphoma
    FS Abas, A Shana’ah, B Christian, R Hasserjian, A Louissaint Jr, et al.
    Cytometry Part A 91 (6), 609-621
  • A computational framework to detect normal and tuberculosis Infected lung from H&E-stained whole slide images
    MKK Niazi, G Beamer, M Gurcan
    SPIE Medical Imaging
  • Interactive Image Compression for Big Data Image Analysis: Application to Hotspot Detection in Breast Cancer
    MKK Niazi, Y Lin, F Liu, A Ashoka, M Marcellin, G Tozbikian
    Submitted for Journal publication
  • PMC4696238
    B Sahiner, W Chen, MKK Niazi, G Lozanski, M Gurcan
    Cytopathology 27 (6), 390-397
  • Advancing clinicopathologic diagnosis of high-risk neuroblastoma using computerized image analysis and proteomic profiling
    MKK Niazi, JH Chung, KJ Heaton-Johnson, D Martinez, R Castellanos, et al.
    Pediatric and developmental pathology: the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society
  • Computer-assisted bladder cancer grading: α-shapes for color space
    MKK Niazi, et al.
  • Analysis Of Prostate Adenocarcinoma Whole Transcriptome Shotgun Sequencing
    K Yao, MKK Niazi, et al
    OSUWMC 13th Trainee Research Day
  • Grading Vascularity from Histopathological Images based on Traveling Salesman Distance and Vessel Size
    MKK Niazi, J Hemminger, H Kurt, G Lozanski, MN Gurcan
    Proceedings of SPIE Medical Imaging, San Diego, USA
  • Hot spot detection for breast cancer in Ki-67 stained slides: Image dependent filtering approach
    MKK Niazi, E Downs-Kelly, MN Gurcan
    Proceedings of SPIE Medical Imaging, San Diego, USA
  • Histopathological image analysis for centroblasts classification through dimensionality reduction approaches
    E Kornaropoulos, MKK Niazi, G Lozanski, MN Gurcan
    Journal of Cytometry Part A
  • Detecting and characterizing cellular responses to Mycobacterium tuberculosis from histology slides
    MKK Niazi, G Beamer, MN Gurcan
    Journal of Cytometry Part A 85 (2), 151-161
  • Cell Density estimation from digital Histology: Application to Tuberculosis
    MKK Niazi, B Gillian, MN Gurcan
    OSUWMC 12th Trainee Research Day, BRT, The Ohio State University
  • Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study
    MKK Niazi, M Pennell, C Elkins, J Hemminger, M Jin, S Kirby, H Kurt, et al.
    Proc SPIE 8676, 86760I
  • An automated method for counting cytotoxic T-cells from CD8 stained images of renal biopsies
    MKK Niazi, AA Satoskar, MN Gurcan
    Medical Imaging 2013: Digital Pathology 8676, 60-69
  • Impact of Diffusion Barriers to Small Cytotoxic Molecules on the Efficacy of Immunotherapy in Breast Cancer
    VC Hiramnoy Das, Zhihui Wang, M Khalid Khan Niazi
    PLOS ONE
  • Horizontal features based illumination normalization method for face recognition
    MT Ibrahim, L Guan, MKK Niazi
    2011 7th International Symposium on Image and Signal Processing and Analysis
  • Bias field correction using grey-weighted distance transform applied on MR volumes
    MKK Niazi, I Nystrom, MT Ibrahim, L Guan
    Biomedical Imaging: From Nano to Macro, 2011
  • An Iterative Method for Intensity Inhomogeneity Correction based on the Greyweighted distance transform of the magnitude spectrum
    M Niazi, MT Ibrahim, L Guan, I Nyström
    Image Filtering Methods for Biomedical Applications
  • Robust signal generation and analysis of rat embryonic heart rate in vitro using laplacian eigenmaps and empirical mode decomposition
    M Niazi, M Ibrahim, M Nilsson, AC Sköld, L Guan, I Nyström
    Computer Analysis of Images and Patterns, 523-530
  • The effect of drugs with ion channel-blocking activity on the early embryonic rat heart
    D Abela, H Ritchie, D Ababneh, C Gavin, MF Nilsson, MK Khan, et al.
    Birth Defects Research Part B: Developmental and Reproductive Toxicology
  • Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification
    MT Ibrahim, MA Khan, KS Alimgeer, MK Khan, IA Taj, L Guan
    Pattern Recognition 43 (8), 2817-2832
  • A modified particle swarm optimization applied in image registration
    MK Khan, I Nyström
    2010 20th International Conference on Pattern Recognition, 2302-2305
  • Rodent Organogenesis Toxicology Ontology (ROTO): Linking Morphology to Embryotoxicity. ,
    MF Nilsson, LD Birger Scholz Khan M. Khalid, Aldert H. Piersma
    Teratology Society 50th Annual Conference, Louisville KY, USA
  • Rodent Organogenesis Toxicology Ontology (ROTO): Linking Morphology to Embryotoxicity
    MF Nilsson, B Scholz, KM Khan, AH Piersma, L Dencker
    BIRTH DEFECTS RESEARCH PART A-CLINICAL AND MOLECULAR TERATOLOGY 88 (5), 382-382
  • Improved methodology for identifying the teratogenic potential in early drug development of hERG channel blocking drugs
    MF Nilsson, C Danielsson, AC Sköld, A Johansson, B Blomgren, J Wilson, et al.
    Reproductive toxicology 29 (2), 156-163
  • Using a ring-shaped region around the optic disc in retinal image registration of Glaucoma patients
    Bettina Selig, M Khalid Khan Niazi, Ingela Nyström
    Symposium on Image Analysis, Sweden, SSBA 2010, 35-38
  • Image Registration using Particle swarm optimization approach
    NI M Khalid Khan Niazi, Jens Hedrich
    Symposium on Image Analysis, Sweden, SSBA 2010, 31-34
  • Fully Automatic Heart Beat Rate Determination in Digital Video Recordings of Rat Embryos
    M Khalid Khan, MF Nilsson, BR Danielsson, E Bengtsson
    Transactions on Mass-Data Analysis of Images and Signals 1 (2), 132-146
  • Measuring Heart Rate from Rat Embryo Videos
    MKK Niazi, E Bengtsson
    Swedish Symposium on Image Analysis, 35-38
  • Measuring Heart Rate from Rat Embryo Videos
    EB M Khalid Khan Niazi
    Symposium on Image Analysis, Sweden, SSBA 2008, 35-38,
  • Fully automatic heart beat rate determination in digital video recordings of rat embryos
    M Khan, M Nilsson, B Danielsson, E Bengtsson
    Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology
  • Endothelial Cell Image Enhancement Using Non-Subsampled Image Pyramid
    MAU Khan, MK Khan, MA Khan, MT Ibrahim
    Information Technology Journal 6 (7)
  • Accuracy of ultrasonography in detection of severe liver fibrosis/cirrhosis chronic liver disease
    M Niazi, MU Khan, A Ghaffar, Z Amin
    Medical Forum Monthly, 2-7
  • Endothelial cell image enhancement using decimation-free directional filter banks
    M Aurangzeb Khan, M Khalid Khan, MAU Khan, S Lee
    Circuits and Systems, 2006. APCCAS 2006.
  • Signature verification using velocity-based directional filter bank
    M Khalid Khan, M Aurangzeb Khan, MAU Khan, S Lee
    Circuits and Systems, 2006. APCCAS 2006.
  • Velocity-image model for online signature verification
    MAU Khan, MKK Niazi, MA Khan
    Image Processing, IEEE Transactions on 15 (11), 3540-3549
  • Fingerprint image enhancement using decimation free directional adaptive mean filtering
    M Ibrahim, I Taj, M Khan, M Khan
    Computer Vision, Graphics and Image Processing, 950-961
  • On-line signature verification by exploiting inter-feature dependencies
    M Khalid Khan, M Aurangzeb Khan, MAU Khan, I Ahmad
    Pattern Recognition, 2006. ICPR 2006
  • Comparative analysis of decimation-free directional filter bank with directional filter bank: in context of image enhancement
    MAU Khan, MK Khan, MA Khan
    2005 Pakistan Section Multitopic Conference, 1-8
  • Cross correlation measure for decision fusion among multiple face classifiers
    MAU Khan, MT Ibrahim, MK Khan, MA Khan
    Emerging Technologies, 2005. Proceedings of the IEEE Symposium on, 126-131
  • Fingerprint image enhancement using decimation-free directional filter bank
    MAU Khan, MK Khan, MA Khan
    Information Technology Journal 4 (1), 16-20
  • Improved PCA based face recognition using directional filter bank
    MAU Khan, MK Khan, MA Khan, MA Khan, MT Ibrahim, MK Ahmed, JA Baig
    Multitopic Conference, 2004. Proceedings of INMIC 2004. 8th International
  • Coronary angiogram image enhancement using decimation-free directional filter banks
    MAU Khan, MK Khan, MA Khan
    Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP’04)
  • Principal component analysis of directional images for face recognition
    MAU Khan, MK Khan, MA Khan, MT Ibrahim, MK Ahmed, JA Baig
    Inform. Technol. J 3, 290-295
  • A Decimation Free Directional Filter Banks for Medical Image Enhancement
    MA Khan, MK Khan
    Information Technology Journal 3 (2), 146-149
  • Improved fingerprint identification using directional filter banks
    MA Khan, MK Khan, MA Khan
    7th International Multi Topic Conference, 2003. INMIC 2003., 49-54
  • Patch-wise transformations as a mechanism for slide-level augmentation in multiple instance learning
    TE Tavolara, MKK Niazi, MN Gurcan
  • Transactions on Mass-Data Analysis of Images and Signals
    P Perner, MK Khan, MF Nilsson, BR Danielsson, E Bengtsson, C Plata, et al.