Jump to Content
Zaid Nabulsi

Zaid Nabulsi

Zaid is currently a senior machine learning engineer at Google, interested in large language models and healthcare. Previously, Zaid worked on an AI research team at Meta and completed an M.S and a B.S. at Stanford University, both in computer science.
Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan
    Atilla Kiraly
    Corbin Cunningham
    Ryan Najafi
    Jie Yang
    Chuck Lau
    Diego Ardila
    Scott Mayer McKinney
    Rory Pilgrim
    Mozziyar Etemadi
    Sunny Jansen
    Lily Peng
    Shravya Shetty
    Neeral Beladia
    Krish Eswaran
    Radiology: Artificial Intelligence (2024)
    Preview abstract Lung cancer is the leading cause of cancer death world-wide with 1.8 million deaths in 20201. Studies have concluded that low-dose computed tomography lung cancer screening can reduce mortality by up to 61%2 and updated 2021 US guidelines expanded eligibility. As screening efforts rise, AI can play an important role, but must be unobtrusively integrated into existing clinical workflows. In this work, we introduce a state-of-the-art, cloud-based AI system providing lung cancer risk assessments without requiring any user input. We demonstrate its efficacy in assisting lung cancer screening under both US and Japanese screening settings using different patient populations and screening protocols. Technical improvements over a previously described system include a focus on earlier cancer detection for improved accuracy, introduction of an effective assistive user interface, and a system designed to integrate into typical clinical workflows. The stand-alone AI system was evaluated on 3085 individuals achieving area under the curve (AUC) scores of 91.7% (95%CI [89.6, 95.2]), 93.3% (95%CI [90.2, 95.7]), and 89.1% (95%CI [77.7, 97.3]) on three datasets (two from US and one from Japan), respectively. To evaluate the system’s assistive ability, we conducted two retrospective multi-reader multi-case studies on 627 cases read by experienced board certified radiologists (average 20 years of experience [7,40]) using local PACS systems in the respective US and Japanese screening settings. The studies measured the reader’s level of suspicion (LoS) and categorical responses for scores and management recommendations under country-specific screening protocols. The radiologists’ AUC for LoS increased with AI assistance by 2.3% (95%CI [0.1-4.5], p=0.022) for the US study and by 2.3% (95%CI [-3.5-8.1], p=0.179) for the Japan study. Specificity for recalls increased by 5.5% (95%CI [2.7-8.5], p<0.0001) for the US and 6.7% (95%CI [4.7-8.7], p<0.0001) for the Japan study. No significant reduction in other metrics occured. This work advances the state-of-the-art in lung cancer detection, introduces generalizable interface concepts that can be applicable to similar AI applications, and demonstrates its potential impact on diagnostic AI in global lung cancer screening with results suggesting a substantial drop in unnecessary follow-up procedures without impacting sensitivity. View details
    Preview abstract Health-related acoustic signals, such as cough and breathing sounds, are relevant for medical diagnosis and continuous health monitoring. Most existing machine learning approaches for health acoustics are trained and evaluated on specific tasks, limiting their generalizability across various healthcare applications. In this paper, we leverage a self-supervised learning framework, SimCLR with a Slowfast NFNet backbone, for contrastive learning of health acoustics. A crucial aspect of optimizing Slowfast NFNets for this application lies in identifying effective audio augmentations. We conduct an in-depth analysis of various audio augmentation strategies and demonstrate that an appropriate augmentation strategy enhances the performance of the Slowfast NFNet audio encoder across a diverse set of health acoustic tasks. Our findings reveal that when augmentations are combined, they can produce synergistic effects that exceed the benefits seen when each is applied individually. View details
    Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists
    Sahar Kazemzadeh
    Jin Yu
    Shahar Jamshy
    Rory Pilgrim
    Christina Chen
    Neeral Beladia
    Chuck Lau
    Scott Mayer McKinney
    Thad Hughes
    Atilla Peter Kiraly
    Sreenivasa Raju Kalidindi
    Monde Muyoyeta
    Jameson Malemela
    Ting Shih
    Lily Hao Yi Peng
    Kat Chou
    Cameron Chen
    Krish Eswaran
    Shravya Ramesh Shetty
    Radiology (2022)
    Preview abstract Background: The World Health Organization (WHO) recommends chest radiography to facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise remains limited in many regions. Purpose: To develop a deep learning system (DLS) to detect active pulmonary TB on chest radiographs and compare its performance to that of radiologists. Materials and Methods: A DLS was trained and tested using retrospective chest radiographs (acquired between 1996 and 2020) from 10 countries. To improve generalization, large-scale chest radiograph pretraining, attention pooling, and semisupervised learning (“noisy-student”) were incorporated. The DLS was evaluated in a four-country test set (China, India, the United States, and Zambia) and in a mining population in South Africa, with positive TB confirmed with microbiological tests or nucleic acid amplification testing (NAAT). The performance of the DLS was compared with that of 14 radiologists. The authors studied the efficacy of the DLS compared with that of nine radiologists using the Obuchowski-Rockette-Hillis procedure. Given WHO targets of 90% sensitivity and 70% specificity, the operating point of the DLS (0.45) was prespecified to favor sensitivity. Results: A total of 165 754 images in 22 284 subjects (mean age, 45 years; 21% female) were used for model development and testing. In the four-country test set (1236 subjects, 17% with active TB), the receiver operating characteristic (ROC) curve of the DLS was higher than those for all nine India-based radiologists, with an area under the ROC curve of 0.89 (95% CI: 0.87, 0.91). Compared with these radiologists, at the prespecified operating point, the DLS sensitivity was higher (88% vs 75%, P < .001) and specificity was noninferior (79% vs 84%, P = .004). Trends were similar within other patient subgroups, in the South Africa data set, and across various TB-specific chest radiograph findings. In simulations, the use of the DLS to identify likely TB-positive chest radiographs for NAAT confirmation reduced the cost by 40%–80% per TB-positive patient detected. Conclusion: A deep learning method was found to be noninferior to radiologists for the determination of active tuberculosis on digital chest radiographs. View details
    Simplified Transfer Learning for Chest X-ray Models using Less Data
    Christina Chen
    AJ Maschinot
    Jenny Huang
    Chuck Lau
    Sreenivasa Raju Kalidindi
    Mozziyar Etemadi
    Florencia Garcia-Vicente
    David Melnick
    Krish Eswaran
    Neeral Beladia
    Dilip Krishnan
    Shravya Ramesh Shetty
    Radiology (2022)
    Preview abstract Background: Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and standard of care during the COVID-19 pandemic. A common partial mitigation is transfer learning by pretraining a “generic network” on a large nonmedical data set and then fine-tuning on a task-specific radiology data set. Purpose: To reduce data set size requirements for chest radiography deep learning models by using an advanced machine learning approach (supervised contrastive [SupCon] learning) to generate chest radiography networks. Materials and Methods: SupCon helped generate chest radiography networks from 821 544 chest radiographs from India and the United States. The chest radiography networks were used as a starting point for further machine learning model development for 10 prediction tasks (eg, airspace opacity, fracture, tuberculosis, and COVID-19 outcomes) by using five data sets comprising 684 955 chest radiographs from India, the United States, and China. Three model development setups were tested (linear classifier, nonlinear classifier, and fine-tuning the full network) with different data set sizes from eight to 85. Results: Across a majority of tasks, compared with transfer learning from a nonmedical data set, SupCon reduced label requirements up to 688-fold and improved the area under the receiver operating characteristic curve (AUC) at matching data set sizes. At the extreme low-data regimen, training small nonlinear models by using only 45 chest radiographs yielded an AUC of 0.95 (noninferior to radiologist performance) in classifying microbiology-confirmed tuberculosis in external validation. At a more moderate data regimen, training small nonlinear models by using only 528 chest radiographs yielded an AUC of 0.75 in predicting severe COVID-19 outcomes. Conclusion: Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations. View details
    Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19
    Shahar Jamshy
    Charles Lau
    Eddie Santos
    Atilla Peter Kiraly
    Jie Yang
    Rory Pilgrim
    Sahar Kazemzadeh
    Jin Yu
    Lily Hao Yi Peng
    Krish Eswaran
    Neeral Beladia
    Cameron Chen
    Shravya Ramesh Shetty
    Scientific Reports (2021)
    Preview abstract Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to detect every possible condition by building multiple separate systems, each of which detects one or more pre-specified conditions. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For training and tuning the system, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7–28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist. Lastly, to facilitate the continued development of AI models for CXR, we release our collected labels for the publicly available dataset. View details
    No Results Found