arXiv subjects

Ahmed Hosny

Publications and source records attributed to Ahmed Hosny.

5 recordsLinked to original sources

arXiv2026

Crustal and upper mantle model of the Middle East based on full-waveform inversion

We present MEAD-M20, a new tomographic model of the Middle East and its surrounding regions, including Anatolia, Iran, and the Caucasus. The model is developed within a full-waveform inversion framework, based on 3D wavefield simulations and the adjoint method, after 20 iterations, utilizing an extensive dataset from permanent and temporary stations available from EarthScope and regional networks. Starting from the global FWI model GLAD-M25 on a 60°x 60° regional mesh, we invert 210 regional earthquakes recorded by 1,215 stations to obtain the P- and S-wave model with transverse isotropy in the upper mantle. For the first 12 iterations, we combine multitaper traveltime measurements of 15-50 s body waves and 50-100 s body and surface waves on three components. We use a refined crustal mesh to better sample the crust after the 12th iteration and gradually decrease the minimum surface-wave period to 30 s. MEAD-M20 provides a self-consistent P- and S-wave model ready for seismic wave simulations, which is essential for accurate earthquake location, source parameter estimation, and seismic hazard assessment in the geologically and tectonically complex region. MEAD-M20 reveals several important geodynamical and tectonic features, including local mantle plumes beneath the Arabian Plate, Jordan, and the Levant, characterized by low-velocity anomalies and likely associated with volcanism in the Harrats, Jordan, and the Karacadag regions. In addition to the active subduction and rifting in the area, the model clearly identifies remnants of the Tethys Ocean beneath Eastern Anatolia, which become progressively shallower toward the Makran region in the south, consistent with the subduction history along the Bitlis-Zagros suture zone. We also observe lithospheric-scale low-velocity anomalies associated with the North and East Anatolian faults, extending to depths of approximately 200 km.

physics.geo-ph↗

arXiv2021

Deep learning-based detection of intravenous contrast in computed tomography scans

Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) and lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans manually annotated for contrast presence by clinical experts. Five different DL models were adopted and trained in HN training datasets for slice-level contrast detection. Model performances were evaluated on a hold-out set and on an independent validation set from another institution. DL models was then fine-tuned on chest CT data and externally validated on a separate chest CT dataset. Results: Initial DICOM metadata tags for IV contrast were missing or erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the best overall detection performance. For HN scans, AUC was 0.996 in the internal validation set (n = 216) and 1.0 in the external validation set (n = 595). The fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation set (n = 53), and AUC: 0.980 for the external validation set (n = 402). Conclusion: The DL model could accurately detect IV contrast in both HN and chest CT scans with near-perfect performance.

eess.IV↗

arXiv2020

The importance of transparency and reproducibility in artificial intelligence research

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field.

stat.AP↗

arXiv2019

ModelHub.AI: Dissemination Platform for Deep Learning Models

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit leading to major performance breakthroughs in some tasks, effective dissemination of deep learning algorithms remains challenging, inhibiting reproducibility and benchmarking studies, impeding further validation, and ultimately hindering their effectiveness in the cumulative scientific progress. In developing a platform for sharing research outputs, we present ModelHub.AI (www.modelhub.ai), a community-driven container-based software engine and platform for the structured dissemination of deep learning models. For contributors, the engine controls data flow throughout the inference cycle, while the contributor-facing standard template exposes model-specific functions including inference, as well as pre- and post-processing. Python and RESTful Application programming interfaces (APIs) enable users to interact with models hosted on ModelHub.AI and allows both researchers and developers to utilize models out-of-the-box. ModelHub.AI is domain-, data-, and framework-agnostic, catering to different workflows and contributors' preferences.

cs.LG↗

arXiv2018

The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards

Artificial intelligence (AI) systems built on incomplete or biased data will often exhibit problematic outcomes. Current methods of data analysis, particularly before model development, are costly and not standardized. The Dataset Nutrition Label (the Label) is a diagnostic framework that lowers the barrier to standardized data analysis by providing a distilled yet comprehensive overview of dataset "ingredients" before AI model development. Building a Label that can be applied across domains and data types requires that the framework itself be flexible and adaptable; as such, the Label is comprised of diverse qualitative and quantitative modules generated through multiple statistical and probabilistic modelling backends, but displayed in a standardized format. To demonstrate and advance this concept, we generated and published an open source prototype with seven sample modules on the ProPublica Dollars for Docs dataset. The benefits of the Label are manyfold. For data specialists, the Label will drive more robust data analysis practices, provide an efficient way to select the best dataset for their purposes, and increase the overall quality of AI models as a result of more robust training datasets and the ability to check for issues at the time of model development. For those building and publishing datasets, the Label creates an expectation of explanation, which will drive better data collection practices. We also explore the limitations of the Label, including the challenges of generalizing across diverse datasets, and the risk of using "ground truth" data as a comparison dataset. We discuss ways to move forward given the limitations identified. Lastly, we lay out future directions for the Dataset Nutrition Label project, including research and public policy agendas to further advance consideration of the concept.

cs.DB↗