| 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, 2022 | 39 | 2022 |
| 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, ... Plos one 18 (4), e0283562, 2023 | 17 | 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, 2023 | 16 | 2023 |
| 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 (0010-4825), 107607, 2023 | 9 | 2023 |
| 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, 2023 | 8 | 2023 |
| 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 | 3 | 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 | 2 | 2024 |
| 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, 2023 | 2 | 2023 |
| 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, EB Balaban, I Aloglu, M Dogan, ... PloS one 19 (12), e0314450, 2024 | | 2024 |
| 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 | | 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 | | 2024 |
| Adapting Segment Anything Model for Tumor Bud Segmentation on Hematoxylin and Eosin Images of Colorectal Cancer Z Su, MD Wei Chen, U Sajjad Archives of Pathology & Laboratory Medicine 148 (9), E183-E183, 2024 | | 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 | | 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 | | 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 | | 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, ... Proceedings of SPIE--the International Society for Optical Engineering 12933, 2024 | | 2024 |
| Shamap: Shape-based Manifold Learning F Fan, Z Su, Y Teng, G Wang arXiv preprint arXiv:1802.05386, 2018 | | 2018 |
| Automatic Computerized Identification Of Difficult Intubation From Facial Photographs S Segal, K Niazi, Z Su Anesth Analg 112 (1), 84-93, 2011 | | 2011 |
M Khalid Khan NiaziThe Ohio State UniversityVerified email at osumc.edu
