Selected Publications

2021
Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, and Faisal Mahmood. 2021. “AI-based pathology predicts origins for cancers of unknown primary.” Nature, Pp. 1–25.
Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood. 2021. “Data-efficient and weakly supervised computational pathology on whole-slide images.” Nature Biomedical Engineering, Pp. 1–16.
Kutsev Bengisu Ozyoruk, Guliz Irem Gokceler, Taylor L Bobrow, Gulfize Coskun, Kagan Incetan, Yasin Almalioglu, Faisal Mahmood, Eva Curto, Luis Perdigoto, Marina Oliveira, and others. 2021. “EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos.” Medical image analysis, Pp. 102058.
Kağan İncetan, Ibrahim Omer Celik, Abdulhamid Obeid, Guliz Irem Gokceler, Kutsev Bengisu Ozyoruk, Yasin Almalioglu, Richard J Chen, Faisal Mahmood, Hunter Gilbert, Nicholas J Durr, and others. 2021. “VR-Caps: A Virtual Environment for Capsule Endoscopy.” Medical image analysis, 70, Pp. 101990.
2020
Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce, Kagan Incetan, Guliz Irem Gokceler, Muhammed Ali Simsek, Kivanc Ararat, Richard J Chen, Nicholas J Durr, Faisal Mahmood, and others. 2020. “EndoL2H: Deep Super-Resolution for Capsule Endoscopy.” IEEE Transactions on Medical Imaging, 39, 12, Pp. 4297–4309.
Richard J Chen, Ming Y Lu, Jingwen Wang, Drew FK Williamson, Scott J Rodig, Neal I Lindeman, and Faisal Mahmood. 2020. “Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis.” IEEE Transactions on Medical Imaging.
2019
Faisal Mahmood, Richard Chen, Daniel Borders, Gregory N McKay, Kevan Salimian, Alexander Baras, and Nicholas J Durr. 2019. “Adversarial U-net with spectral normalization for histopathology image segmentation using synthetic data.” In Medical Imaging 2019: Digital Pathology, 10956: Pp. 109560N. International Society for Optics and Photonics.
Faisal Mahmood, Daniel Borders, Richard Chen, Gregory N McKay, Kevan J Salimian, Alexander Baras, and Nicholas J Durr. 2019. “Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images.” IEEE Trans Med Imaging.Abstract
Nuclei segmentation is a fundamental task for various computational pathology applications, including nuclei morphology analysis, cell type classification, and cancer grading. Deep learning has emerged as a powerful approach to segmenting nuclei, but the accuracy of convolutional neural networks (CNNs) depends on the volume and quality of labeled histopathology data for training. In particular, conventional CNN-based approaches lack structured prediction capabilities, which are required to distinguish overlapping and clumped nuclei. Here, we present an approach to nuclei segmentation that overcomes these challenges by utilizing a conditional generative adversarial network (cGAN) trained with synthetic and real data. We generate a large dataset of H&E training images with perfect nuclei segmentation labels using an unpaired GAN framework. This synthetic data along with real histopathology data from six different organs are used to train a conditional GAN with spectral normalization and gradient penalty for nuclei segmentation. This adversarial regression framework enforces higher-order spacial-consistency when compared to conventional CNN models. We demonstrate that this nuclei segmentation approach generalizes across different organs, sites, patients and disease states, and outperforms conventional approaches, especially in isolating individual and overlapping nuclei.
Taylor L Bobrow, Faisal Mahmood, Miguel Inserni, and Nicholas J Durr. 2019. “DeepLSR: a deep learning approach for laser speckle reduction.” Biomed Opt Express, 10, 6, Pp. 2869-2882.Abstract
Speckle artifacts degrade image quality in virtually all modalities that utilize coherent energy, including optical coherence tomography, reflectance confocal microscopy, ultrasound, and widefield imaging with laser illumination. We present an adversarial deep learning framework for laser speckle reduction, called DeepLSR (https://durr.jhu.edu/DeepLSR), that transforms images from a source domain of coherent illumination to a target domain of speckle-free, incoherent illumination. We apply this method to widefield images of objects and tissues illuminated with a multi-wavelength laser, using light emitting diode-illuminated images as ground truth. In images of gastrointestinal tissues, DeepLSR reduces laser speckle noise by 6.4 dB, compared to a 2.9 dB reduction from optimized non-local means processing, a 3.0 dB reduction from BM3D, and a 3.7 dB reduction from an optical speckle reducer utilizing an oscillating diffuser. Further, DeepLSR can be combined with optical speckle reduction to reduce speckle noise by 9.4 dB. This dramatic reduction in speckle noise may enable the use of coherent light sources in applications that require small illumination sources and high-quality imaging, including medical endoscopy.
Mason T Chen, Faisal Mahmood, Jordan A Sweer, and Nicholas J Durr. 2019. “GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images.” arXiv preprint arXiv:1906.05360.
Gregory N McKay, Faisal Mahmood, and Nicholas J Durr. 2019. “Large dynamic range autorefraction with a low-cost diffuser wavefront sensor.” Biomed Opt Express, 10, 4, Pp. 1718-1735.Abstract
Wavefront sensing with a thin diffuser has emerged as a potential low-cost alternative to a lenslet array for aberrometry. Here we show that displacement of caustic patterns can be tracked for estimating wavefront gradient in a diffuser wavefront sensor (DWFS), enabling large dynamic-range wavefront measurements with sufficient accuracy for eyeglass prescription measurements. We compare the dynamic range, repeatability, precision, and number of resolvable prescriptions of a DWFS to a Shack-Hartmann wavefront sensor (SHWFS) for autorefraction measurement. We induce spherical and cylindrical errors in a model eye and use a multi-level Demon's non-rigid registration algorithm to estimate caustic displacements relative to an emmetropic model eye. When compared to spherical error measurements with the SHWFS using a laser diode with a laser speckle reducer, the DWFS demonstrates a ∼5-fold improvement in dynamic range (-4.0 to +4.5 D vs. -22.0 to +19.5 D) with less than half the reduction in resolution (0.072 vs. 0.116 D), enabling a ∼3-fold increase in the number of resolvable prescriptions (118 vs. 358). In addition to being lower-cost, the unique, non-periodic nature of the caustic pattern formed by a diffuser enables a larger dynamic range of aberration measurements compared to a lenslet array.
Mehmet Turan, Yasin Almalioglu, Hunter B Gilbert, Faisal Mahmood, Nicholas J Durr, Helder Araujo, Alp Eren Sarı, Anurag Ajay, and Metin Sitti. 2019. “Learning to Navigate Endoscopic Capsule Robots.” IEEE Robotics and Automation Letters, 4, 3, Pp. 3075–3082.
Richard J Chen, Taylor L Bobrow, Thomas Athey, Faisal Mahmood, and Nicholas J Durr. 2019. “SLAM Endoscopy enhanced by adversarial depth prediction.” KDD Workshop on Applied Data Science for Healthcare 2019.
2018
Faisal Mahmood, Märt Toots, Lars-göran Wallentin Öfverstedt, and Bo Ulf Skoglund. 2018. “2d discrete fourier transform with simultaneous edge artifact removal for real-time applications”.
Faisal Mahmood, Nauman Shahid, Ulf Skoglund, and Pierre Vandergheynst. 2018. “Adaptive graph-based total variation for tomographic reconstructions.” IEEE Signal Processing Letters, 25, 5, Pp. 700–704.
Faisal Mahmood and Nicholas J Durr. 2018. “Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy.” Med Image Anal, 48, Pp. 230-243.Abstract
Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the identification and removal of premalignant lesions through optical colonoscopy. Unfortunately, conventional colonoscopy misses more than 20% of the polyps that should be removed, due in part to poor contrast of lesion topography. Imaging depth and tissue topography during a colonoscopy is difficult because of the size constraints of the endoscope and the deforming mucosa. Most existing methods make unrealistic assumptions which limits accuracy and sensitivity. In this paper, we present a method that avoids these restrictions, using a joint deep convolutional neural network-conditional random field (CNN-CRF) framework for monocular endoscopy depth estimation. Estimated depth is used to reconstruct the topography of the surface of the colon from a single image. We train the unary and pairwise potential functions of a CRF in a CNN on synthetic data, generated by developing an endoscope camera model and rendering over 200,000 images of an anatomically-realistic colon.We validate our approach with real endoscopy images from a porcine colon, transferred to a synthetic-like domain via adversarial training, with ground truth from registered computed tomography measurements. The CNN-CRF approach estimates depths with a relative error of 0.152 for synthetic endoscopy images and 0.242 for real endoscopy images. We show that the estimated depth maps can be used for reconstructing the topography of the mucosa from conventional colonoscopy images. This approach can easily be integrated into existing endoscopy systems and provides a foundation for improving computer-aided detection algorithms for detection, segmentation and classification of lesions.
Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, and Nicholas J Durr. 2018. “Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images.” Physics in Medicine & Biology, 63, 18, Pp. 185012.
Faisal Mahmood and Nicholas J Durr. 2018. “Deep learning-based depth estimation from a synthetic endoscopy image training set.” In Medical Imaging 2018: Image Processing, 10574: Pp. 1057421. International Society for Optics and Photonics.
Faisal Mahmood, Lars-Göran Öfverstedt, Märt Toots, Gunnar Wilken, and Ulf Skoglund. 2018. “An extended field-based method for noise removal from electron tomographic reconstructions.” IEEE Access, 6, Pp. 17326–17339.
Faisal Mahmood and Nicholas J Durr. 2018. “Topographical reconstructions from monocular optical colonoscopy images via deep learning.” In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Pp. 216–219. IEEE.

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