Prof. Muhammad Khalid Khan Niazi Profile

Prof. Muhammad Khalid Khan Niazi

at Wake Forest Univ School of Medicine

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Author

Publications (37)

Proceedings Article | 3 April 2024 Paper

Deep-ODX: an efficient deep learning tool to risk stratify breast cancer patients from histopathology images

Ziyu Su, Amanda Rosen, Robert Wesolowski, Gary Tozbikian, M. Khalid Khan Niazi, Metin Gurcan

Proceedings Volume 12933, 1293307 (2024) https://doi.org/10.1117/12.3006272

KEYWORDS: Breast cancer, Deep learning, Histopathology, Machine learning, Chemotherapy, Feature extraction, Data modeling, Neural networks

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Proceedings Article | 3 April 2024 Presentation + Paper

Few-shot tumor bud segmentation using generative model in colorectal carcinoma

Ziyu Su, Wei Chen, Preston Leigh, Usama Sajjad, Shuo Niu, Mostafa Rezapour, Wendy Frankel, Metin Gurcan, M. Khalid Khan Niazi

Proceedings Volume 12933, 129330A (2024) https://doi.org/10.1117/12.3006418

KEYWORDS: Image segmentation, Tumors, Data modeling, Cancer detection, Deep learning

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Proceedings Article | 3 April 2024 Presentation + Paper

Enhancing colorectal cancer tumor bud detection using deep learning from routine H&E-stained slides

Usama Sajjad, Wei Chen, Mostafa Rezapour, Ziyu Su, Thomas Tavolara, Wendy Frankel, Metin Gurcan, M. Khalid Khan Niazi

Proceedings Volume 12933, 129330T (2024) https://doi.org/10.1117/12.3006796

KEYWORDS: Tumors, Cancer detection, Deep learning, Colorectal cancer, Machine learning

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Proceedings Article | 3 April 2024 Paper

Adapting SAM to histopathology images for tumor bud segmentation in colorectal cancer

Ziyu Su, Wei Chen, Sony Annem, Usama Sajjad, Mostafa Rezapour, Wendy Frankel, Metin Gurcan, M. Khalid Khan Niazi

Proceedings Volume 12933, 129330C (2024) https://doi.org/10.1117/12.3006517

KEYWORDS: Image segmentation, Tumors, Histopathology, Colorectal cancer, Pathology

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Proceedings Article | 3 April 2024 Presentation + Paper

Combining frontal and profile view facial images to predict difficult-to-intubate patients using AI

Ziyu Su, Thomas Tavolara, Usama Sajjad, Metin Gurcan, Scott Segal, M. Khalid Khan Niazi

Proceedings Volume 12927, 129270L (2024) https://doi.org/10.1117/12.3006465

KEYWORDS: Data modeling, Feature extraction, Artificial intelligence, Facial recognition systems, Performance modeling

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Proceedings Article | 3 April 2024 Poster + Paper

Tympanic membrane segmentation of video frames to create composite images using SAM

Seda Camalan, Muhammad Khalid Khan Niazi, Charles Elmaraghy, Aaron Moberly, Metin Gurcan

Proceedings Volume 12927, 1292736 (2024) https://doi.org/10.1117/12.3006926

KEYWORDS: Image segmentation, Video, Ear, Education and training, Diagnostics, Visual process modeling, Transformers, Semantics, Diseases and disorders, Data modeling

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Proceedings Article | 7 April 2023 Poster + Paper

Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models

Ziyu Su, Sandhya Kumar, Thomas Tavolara, Metin Gurcan, Scott Segal, M. Khalid Khan Niazi

Proceedings Volume 12465, 124652P (2023) https://doi.org/10.1117/12.2654353

KEYWORDS: Machine learning, Data modeling, Polysomnography, Education and training, Sleep apnea, Deep learning, Performance modeling, Pulmonary disorders, Neurological disorders, Medicine

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Proceedings Article | 7 April 2023 Presentation + Paper

Background detection affects downstream classification of Camelyon16 whole slide images

Thomas Tavolara, M. Khalid Khan Niazi, Metin Gurcan

Proceedings Volume 12471, 124710O (2023) https://doi.org/10.1117/12.2653882

KEYWORDS: Cancer detection, Tissues, Machine learning, Image classification, Tumors, Cross validation, Feature extraction, Pathology

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Proceedings Article | 7 April 2023 Poster + Paper

Simple patch-wise transformations serve as a mechanism for slide-level augmentation for multiple instance learning applications

Thomas Tavolara, M. Khalid Khan Niazi, Metin Gurcan

Proceedings Volume 12471, 124711E (2023) https://doi.org/10.1117/12.2653878

KEYWORDS: Machine learning, Histopathology, Cross validation, Tumors, Image classification, Feature extraction, Deep learning, Biomedical applications

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Proceedings Article | 7 April 2023 Presentation + Paper

Deep learning to predict the proportion of positive cells in CMYC-stained tissue microarrays of diffuse large B-cell lymphoma

Thomas Tavolara, M. Khalid Khan Niazi, David Jaye, Christopher Flowers, Lee Cooper, Metin Gurcan

Proceedings Volume 12471, 1247104 (2023) https://doi.org/10.1117/12.2654489

KEYWORDS: Tissues, Machine learning, Lymphoma, Deep learning, Cross validation, Tumors, Medicine, Feature extraction

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Proceedings Article | 7 April 2023 Presentation + Paper

The effects of sparsity induction methods on attention-based multiple instance learning applied to Camelyon16

Thomas Tavolara, Metin Gurcan, M. Khalid Khan Niazi

Proceedings Volume 12471, 124710M (2023) https://doi.org/10.1117/12.2653885

KEYWORDS: Machine learning, Tumors, Deep learning, Performance modeling, Pathology, Feature extraction, Neural networks

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Proceedings Article | 7 April 2023 Presentation + Paper

Minimizing the intra-pathologist disagreement for tumor bud detection on H&E images using weakly supervised learning

Thomas Tavolara, Wei Chen, Wendy Frankel, Metin Gurcan, M. Khalid Khan Niazi

Proceedings Volume 12471, 1247113 (2023) https://doi.org/10.1117/12.2653887

KEYWORDS: Tumors, Machine learning, Cancer detection, Cancer, Education and training, Colorectal cancer, Algorithm development

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Proceedings Article | 4 April 2022 Presentation + Paper

Predicting HER2 scores from registered HER2 and H&E images

Thomas Tavolara, M. Khalid Khan Niazi, Gary Tozbikian, Robert Wesolowski, Metin Gurcan

Proceedings Volume 12039, 120390C (2022) https://doi.org/10.1117/12.2612878

KEYWORDS: Tissues, Receptors, Image registration, Cancer, Breast cancer

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Proceedings Article | 4 April 2022 Presentation + Paper

Automatic generation of the ground truth for tumor budding using H&E stained slides

Thomas Tavolara, Arijit Dutta, Martin Burks, Wei Chen, Wendy Frankel, Metin Gurcan, M. Khalid Khan Niazi

Proceedings Volume 12039, 1203909 (2022) https://doi.org/10.1117/12.2612892

KEYWORDS: Tumors, Transformers, Tissues, Image segmentation, Colorectal cancer, Cancer, Image registration

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Proceedings Article | 15 February 2021 Presentation + Paper

Identifying lung imaging biomarkers of BCG vaccination after infection with Mycobacterium tuberculosis

Thomas Tavolara, M. K. K. Niazi, Gillian Beamer, Metin Gurcan

Proceedings Volume 11603, 116030A (2021) https://doi.org/10.1117/12.2581945

KEYWORDS: Lung imaging, Genomics, Lung, Diagnostics, Animal model studies

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Proceedings Article | 15 February 2021 Presentation + Paper

Bladder cancer organoid image analysis: textured-based grading

Seda Camalan, M. Khalid Khan Niazi, Mahesh Devarasetty, Shay Soker, Metin Gurcan

Proceedings Volume 11603, 1160305 (2021) https://doi.org/10.1117/12.2582208

KEYWORDS: Bladder cancer, Image analysis, Image filtering, Tumors, Tissues, Genetics, Collagen, Cancer, Bladder

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Panoptic segmentation of wounds in a pig model

Thomas Tavolara, Adam Jorgensen, Metin Gurcan, Sean Murphy, M. K. Niazi

Proceedings Volume 11597, 115971W (2021) https://doi.org/10.1117/12.2581993

KEYWORDS: Wound healing, Standards development, Image segmentation, Image analysis

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Proceedings Article | 15 February 2021 Presentation + Paper

Grading and localization of histological features for bioengineered kidney constructs

Thomas Tavolara, Gabriel Carreno-Galeano, Metin Gurcan, Sang Jin Lee, M. K. K. Niazi

Proceedings Volume 11603, 1160306 (2021) https://doi.org/10.1117/12.2581949

KEYWORDS: Tissues, Kidney, Transplantation, Visualization, Tissue engineering, Therapeutics, Regenerative medicine, Convolutional neural networks

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Proceedings Article | 15 February 2021 Presentation + Paper

Automated video summarization and label assignment for otoscopy videos using deep learning and natural language processing

Hamidullah Binol, M. Khalid Khan Niazi, Charles Elmaraghy, Aaron Moberly, Metin Gurcan

Proceedings Volume 11601, 116010S (2021) https://doi.org/10.1117/12.2582009

KEYWORDS: Video, Video processing, Diagnostics, Visualization, Ear, Visual process modeling, Instrument modeling, Image segmentation, Composites, Visual analytics

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Proceedings Article | 16 March 2020 Paper

Hotspot detection in pancreatic neuroendocrine images using local depth

Muhammad Khalid Khan Niazi, Katherine Moore, Kenneth Berenhaut, Douglas Hartman, Liron Pantanowitz, Metin Gurcan

Proceedings Volume 11320, 1132008 (2020) https://doi.org/10.1117/12.2550980

KEYWORDS: Tumors, Visualization, Image segmentation, Medicine, Pathology, Cancer, Software development, Tissues, Biopsy, Deconvolution

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Proceedings Article | 16 March 2020 Presentation + Paper

Segmentation of mycobacterium tuberculosis bacilli clusters from acid-fast stained lung biopsies: a deep learning approach

Thomas Tavolara, Muhammad Khalid Khan Niazi, Gillian Beamer, Metin Gurcan

Proceedings Volume 11320, 113200E (2020) https://doi.org/10.1117/12.2549016

KEYWORDS: Bacteria, Image segmentation, Lung, RGB color model, Deconvolution, Tissues, Biopsy, Diagnostics, Image processing

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Proceedings Article | 16 March 2020 Presentation + Paper

A multidimensional scaling and sample clustering to obtain a representative subset of training data for transfer learning-based rosacea lesion identification

Hamidullah Binol, M. Khalid Khan Niazi, Alisha Plotner, Jennifer Sopkovich, Benjamin Kaffenberger, Metin Gurcan

Proceedings Volume 11314, 1131415 (2020) https://doi.org/10.1117/12.2549392

KEYWORDS: Visualization, Convolutional neural networks, Neural networks, Computer aided diagnosis and therapy, Data centers, Dermatology, Machine vision, Computer vision technology, Algorithm development, Health informatics, Image analysis, Pattern recognition

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Proceedings Article | 16 March 2020 Presentation + Paper

Decision fusion on image analysis and tympanometry to detect eardrum abnormalities

Hamidullah Binol, Aaron Moberly, M. Khalid Khan Niazi, Garth Essig, Jay Shah, Charles Elmaraghy, Theodoros Teknos, Nazhat Taj-Schaal, Lianbo Yu, Metin Gurcan

Proceedings Volume 11314, 113141M (2020) https://doi.org/10.1117/12.2549394

KEYWORDS: Ear, Image analysis, Video, Image classification, Visualization, Medicine, Sensors, Diagnostics, Decision support systems, Pattern recognition, Machine learning, Machine vision, Computer aided diagnosis and therapy

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Ear diseases are frequently occurring conditions affecting the majority of the pediatric population, potentially resulting in hearing loss and communication disabilities. The current standard of care in diagnosing ear diseases includes a visual examination of the tympanic membrane (TM) by a medical expert with a range of available otoscopes. However, visual examination is subjective and depends on various factors, including the experience of the expert. This work proposes a decision fusion mechanism to combine predictions obtained from digital otoscopy images and biophysical measurements (obtained through tympanometry) for the detection of eardrum abnormalities. Our database consisted of 73 tympanometry records along with digital otoscopy videos. For the tympanometry aspect, we trained a random forest classifier (RF) using raw tympanometry attributes. Additionally, we mimicked a clinician’s decision on tympanometry findings using the normal range of the tympanogram values provided by a clinical guide. Moreover, we re-trained Inception-ResNet-v2 to classify TM images selected from each otoscopic video. After obtaining predictions from each of three different sources, we performed a majority voting-based decision fusion technique to reach the final decision. Experimental results show that the proposed decision fusion method improved the classification accuracy, positive predictive value, and negative predictive value in comparison to the single classifiers. The results revealed that the accuracies are 64.4% for the clinical evaluations of tympanometry, 76.7% for the computerized analysis of tympanometry data, and 74.0% for the TM image analysis while our decision fusion methodology increases the classification accuracy to 84.9%. To the best of our knowledge, this is the first study to fuse the data from digital otoscopy and tympanometry. Preliminary results suggest that fusing information from different sources of sensors may provide complementary information for accurate and computerized diagnosis of TM-related abnormalities.

Proceedings Article | 16 March 2020 Paper

Identifying bladder layers from H and E images using U-Net image segmentation

Muhammad Khalid Khan Niazi, Enes Yazgan, Cheryl Lee, Anil Parwani, Metin Gurcan

Proceedings Volume 11320, 1132006 (2020) https://doi.org/10.1117/12.2549608

KEYWORDS: Bladder, Image segmentation, Tissues, Bladder cancer, Biopsy, Inflammation, Data modeling, Blood, Visual process modeling, Visualization

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Proceedings Article | 18 March 2019 Paper

Generalization of tumor identification algorithms

Muhammad Khalid Khan Niazi, Thomas Tavolara, Caglar Senaras, Gary Tozbikian, Douglas Hartman, Vidya Arole, Liron Pantanowitz, Metin Gurcan

Proceedings Volume 10956, 109560Z (2019) https://doi.org/10.1117/12.2512911

KEYWORDS: Tumors, Breast cancer, Breast, Tissues, Tumor growth modeling, Image segmentation, Image analysis, Cancer, Biopsy, Pathology

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Proceedings Article | 18 March 2019 Paper

Segmentation of follicles from CD8-stained slides of follicular lymphoma using deep learning

C. Senaras, M. K. Niazi, V. Arole, W. Chen, B. Sahiner, A. Shana’ah, A. Louissaint, R. Hasserjian, G. Lozanski, M. Gurcan

Proceedings Volume 10956, 109560R (2019) https://doi.org/10.1117/12.2512262

KEYWORDS: Lymphoma, Image segmentation, Tumors, Cancer, Pathology, Lymphatic system, Algorithm development, Computer programming, Convolutional neural networks, Health informatics

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Proceedings Article | 18 March 2019 Paper

Colorectal tumor identification by transferring knowledge from pan-cytokeratin to H&E

Thomas Tavolara, Muhammad Khalid Khan Niazi, Wei Chen, Wendy Frankel, Metin Gurcan

Proceedings Volume 10956, 1095614 (2019) https://doi.org/10.1117/12.2513014

KEYWORDS: Tumors, Colorectal cancer, Image segmentation, Pathology, Cancer, Databases, Edge detection, Statistical modeling, Health informatics

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Proceedings Article | 6 March 2018 Paper

An application of transfer learning to neutrophil cluster detection for tuberculosis: efficient implementation with nonmetric multidimensional scaling and sampling

M. Khalid Khan Niazi, Gillian Beamer, Metin Gurcan

Proceedings Volume 10581, 1058108 (2018) https://doi.org/10.1117/12.2292249

KEYWORDS: Lung, Statistical modeling, Data modeling, Medicine, Pathogens, Cancer, Image analysis, Databases, Pathology, Health informatics

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Proceedings Article | 6 March 2018 Paper

Automated T1 bladder risk stratification based on depth of lamina propria invasion from H and E tissue biopsies: a deep learning approach

M. Khalid Khan Niazi, Thomas Tavolara, Vidya Arole, Anil Parwani, Cheryl Lee, Metin Gurcan

Proceedings Volume 10581, 105810H (2018) https://doi.org/10.1117/12.2294552

KEYWORDS: Bladder, Biopsy, Tumors, Bladder cancer, Tissues, Image segmentation, Cancer, Image analysis, Inflammation, Convolution

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Proceedings Article | 1 March 2017 Paper

A computational framework to detect normal and tuberculosis infected lung from H and E-stained whole slide images

M. Khalid Khan Niazi, Gillian Beamer, Metin Gurcan

Proceedings Volume 10140, 101400J (2017) https://doi.org/10.1117/12.2255627

KEYWORDS: Tissues, Lung, Neural networks, Blood, Image processing, Microscopy, Computer aided diagnosis and therapy, RGB color model, Image segmentation, Cancer, Databases, Medicine, Resistance

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Proceedings Article | 23 March 2016 Paper

Computer-assisted bladder cancer grading: α-shapes for color space decomposition

M. Niazi, Anil Parwani, Metin Gurcan

Proceedings Volume 9791, 979107 (2016) https://doi.org/10.1117/12.2216967

KEYWORDS: Bladder cancer, Bladder, Image segmentation, Cancer, Deconvolution, RGB color model, Image classification, Pathology, Computer aided diagnosis and therapy, Copper indium disulfide, Feature extraction, Tumors

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Proceedings Article | 23 March 2016 Paper

Hotspot detection in pancreatic neuroendocrine tumors: density approximation by α-shape maps

M. Niazi, Douglas Hartman, Liron Pantanowitz, Metin Gurcan

Proceedings Volume 9791, 97910B (2016) https://doi.org/10.1117/12.2217687

KEYWORDS: Digestive system, Pathology, Image segmentation, Image classification, Shape analysis, Tumors, Tissues, Image filtering, Cancer, Binary data, Visualization

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Proceedings Article | 19 March 2015 Paper

Characterizing primary refractory neuroblastoma: prediction of outcome by microscopic image analysis

M. Khalid Khan Niazi, Daniel Weiser, Bruce Pawel, Metin Gurcan

Proceedings Volume 9420, 942008 (2015) https://doi.org/10.1117/12.2082095

KEYWORDS: Cancer, Image analysis, Tissues, Oncology, Tumors, Visualization, Image filtering, Nervous system, Surgery, Stem cells

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Proceedings Article | 20 March 2014 Paper

Grading vascularity from histopathological images based on traveling salesman distance and vessel size

M. Khalid Khan Niazi, Jessica Hemminger, Habibe Kurt, Gerard Lozanski, Metin Gurcan

Proceedings Volume 9041, 90410C (2014) https://doi.org/10.1117/12.2043808

KEYWORDS: Blood vessels, Image segmentation, Bone, Biopsy, Tumors, RGB color model, Tissues, Databases, Proteins, Feature extraction

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Vascularity represents an important element of tissue/tumor microenvironment and is implicated in tumor growth, metastatic potential and resistence to therapy. Small blood vessels can be visualized using immunohistochemical stains specific to vascular cells. However, currently used manual methods to assess vascular density are poorly reproducible and are at best semi quantitative. Computer based quantitative and objective methods to measure microvessel density are urgently needed to better understand and clinically utilize microvascular density information. We propose a new method to quantify vascularity from images of bone marrow biopsies stained for CD34 vascular lining cells protein as a model. The method starts by automatically segmenting the blood vessels by methods of maxlink thresholding and minimum graph cuts. The segmentation is followed by morphological post-processing to reduce blast and small spurious objects from the bone marrow images. To classify the images into one of the four grades, we extracted 20 features from the segmented blood vessel images. These features include first four moments of the distribution of the area of blood vessels, first four moments of the distribution of 1) the edge weights in the minimum spanning tree of the blood vessels, 2) the shortest distance between blood vessels, 3) the homogeneity of the shortest distance (absolute difference in distance between consecutive blood vessels along the shortest path) between blood vessels and 5) blood vessel orientation. The method was tested on 26 bone marrow biopsy images stained with CD34 IHC stain, which were evaluated by three pathologists. The pathologists took part in this study by quantifying blood vessel density using gestalt assessment in hematopoietic bone marrow portions of bone marrow core biopsies images. To determine the intra-reader variability, each image was graded twice by each pathologist with two-week interval in between their readings. For each image, the ground truth (grade) was acquired through consensus among the three pathologists at the end of the study. A ranking of the features reveals that the fourth moment of the distribution of the area of blood vessels along with the first moment of the distribution of the shortest distance between blood vessels can correctly grade 68.2% of the bone marrow biopsies, while the intra- and inter-reader variability among the pathologists are 66.9% and 40.0%, respectively.

Proceedings Article | 20 March 2014 Paper

Hot spot detection for breast cancer in Ki-67 stained slides: image dependent filtering approach

M. Khalid Khan Niazi, Erinn Downs-Kelly, Metin Gurcan

Proceedings Volume 9041, 904106 (2014) https://doi.org/10.1117/12.2045586

KEYWORDS: Image filtering, Image segmentation, Breast cancer, Binary data, Visualization, Pathology, Tumors, Tissues, Fourier transforms, Gaussian filters

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Proceedings Article | 29 March 2013 Paper

Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

M. Khalid Khan Niazi, Michael Pennell, Camille Elkins, Jessica Hemminger, Ming Jin, Sean Kirby, Habibe Kurt, Barrie Miller, Elizabeth Plocharczyk, Rachel Roth, Rebecca Ziegler, Arwa Shana’ah, Fred Racke, Gerard Lozanski, Metin Gurcan

Proceedings Volume 8676, 86760I (2013) https://doi.org/10.1117/12.2007909

KEYWORDS: Image segmentation, Tissues, Tumors, Current controlled current source, Pathology, Proteins, Visualization, Image analysis, Information visualization, Computing systems

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Proceedings Article | 29 March 2013 Paper

An automated method for counting cytotoxic T-cells from CD8 stained images of renal biopsies

M. Khalid Khan Niazi, Anjali Satoskar, Metin Gurcan

Proceedings Volume 8676, 867606 (2013) https://doi.org/10.1117/12.2007977

KEYWORDS: Technetium, Biopsy, Image segmentation, Image analysis, RGB color model, Image fusion, Blood, Microscopes, Tissues, Binary data

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Showing 5 of 37 publications

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