arXiv subjects

Omar Metwally

Publications and source records attributed to Omar Metwally.

4 recordsLinked to original sources

arXiv2026

AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology

Whole-slide image (WSI) preprocessing, including tissue detection and patch extraction, is critical computational pathology, yet remains a major bottleneck for large-scale workflows. Existing methods often rely either on threshold-based heuristics that are sensitive to staining variations, tissue fragmentation, and artifacts, or on patch-wise deep learning pipelines with substantially higher computational cost. We present AtlasPatch, a scalable high-throughput WSI preprocessing method built around a foundation-model-based tissue detector that operates at thumbnail resolution: a single thumbnail-level forward pass yields a tissue mask that directly guides patch coordinate generation at the target desired magnification, avoiding repeated patch-level inference. The proposed detector's robustness and efficiency is driven by two coupled contributions: (i) a parameter-efficient adaptation of the SAM2 foundation model that updates only its layer-normalization parameters (0.076% of model weights), and (ii) a curated and semi-manually annotated multi-cohort dataset of 30,000 WSI thumbnail-mask pairs deliberately spanning multiple organs, scanners, tissue appearances, and artifacts. The detector is coupled with pyramid-aware contour mapping from thumbnail to full-resolution slide coordinates, enabling direct patch coordinate generation at the target magnification and parallelized high-throughput patch extraction. AtlasPatch's tissue detection achieves a precision of 0.986 and remains robust across slide variations. Compared with widely used deep-learning preprocessing methods, AtlasPatch is up to 16x faster while preserving downstream multiple-instance learning performance across six slide-level classification tasks. These results position AtlasPatch as a frontier of efficient preprocessing in large-scale computational pathology and pathology foundation models.

eess.IV↗

arXiv2022

Radar-based Respiratory Rate Monitoring in Standing Position

Estimating human vital signs in a contactless non-invasive method using radar provides a convenient method in the medical field to conduct several health checkups easily and quickly. In addition to monitoring while sitting and sleeping, the standing position has aroused interest for both the industrial and medical fields. However, it is more challenging due to the micro motions induced by the body for balancing that may cause false respiratory rate estimation. In this work, we focus on the measurement of the respiratory rate of a standing person accurately with the capability of heavy breath detection and estimation. Multiple estimation approaches are presented and compared, including spectral estimation, deep-learning-based approaches, and adaptive peak selection with Kalman filtering. The latest technique is showing the best performance with an absolute error rate of 1.5 bpm, when compared to a Vernier Go Direct\textsuperscript{\textregistered} respiration belt.

eess.SP↗

arXiv2021

A MIMO Radar-Based Metric Learning Approach for Activity Recognition

Human activity recognition is seen of great importance in the medical and surveillance fields. Radar has shown great feasibility for this field based on the captured micro-Doppler ({\mu}-D) signatures. In this paper, a MIMO radar is used to formulate a novel micro-motion spectrogram for the angular velocity ({\mu}-{\omega}) in non-tangential scenarios. Combining both the {\mu}-D and the {\mu}-{\omega} signatures have shown better performance. Classification accuracy of 88.9% was achieved based on a metric learning approach. The experimental setup was designed to capture micro-motion signatures on different aspect angles and line of sight (LOS). The utilized training dataset was of smaller size compared to the state-of-the-art techniques, where eight activities were captured. A few-shot learning approach is used to adapt the pre-trained model for fall detection. The final model has shown a classification accuracy of 86.42% for ten activities.

eess.SP↗

arXiv2017

On the economics of knowledge creation and sharing

This work bridges the technical concepts underlying distributed computing and blockchain technologies with their profound socioeconomic and sociopolitical implications, particularly on academic research and the healthcare industry. Several examples from academia, industry, and healthcare are explored throughout this paper. The limiting factor in contemporary life sciences research is often funding: for example, to purchase expensive laboratory equipment and materials, to hire skilled researchers and technicians, and to acquire and disseminate data through established academic channels. In the case of the U.S. healthcare system, hospitals generate massive amounts of data, only a small minority of which is utilized to inform current and future medical practice. Similarly, corporations too expend large amounts of money to collect, secure and transmit data from one centralized source to another. In all three scenarios, data moves under the traditional paradigm of centralization, in which data is hosted and curated by individuals and organizations and of benefit to only a small subset of people.

cs.CY↗