Mobile health (mHealth) has emerged as a transformative solution to enhance healthcare accessibility and affordability, particularly in resource-constrained regions and low-to-middle-income countries. mHealth leverages mobile platforms to improve healthcare accessibility, addressing radiologist shortages in low-resource settings by enabling remote diagnosis and consultation through mobile devices. Mobile phones allow healthcare workers to transmit radiographic images, such as chest X-rays (CXR), to specialists or AI-driven models for interpretation. However, AI-based diagnosis using CXR images shared via apps like WhatsApp suffers from reduced predictability and explainability due to compression artifacts, and there is a lack of datasets to systematically study these challenges. To address this, we introduce CheXwhatsApp, a dataset of 141,804 paired original and WhatsApp-compressed CXR images. We present a benchmarking study which shows the dataset improves prediction stability and explainability of state-of-the-art models by up to 80%, while also enhancing localization performance. CheXwhatsApp is open-sourced to support advancements in mHealth applications for CXR analysis1.

In this paper, we present an automated algorithm for detection of blood vessels in 2D-thermographic images for breast cancer screening. Vessel extraction from breast thermal images help in the classification of malignancy as cancer causes increased blood flow at warmer temperatures, additional vessel formation and tortuosity of vessels feeding the cancerous growth. The proposed algorithm uses three enhanced images to detect possible vessel regions based on their intensity and shape. The final vessel detection combines these three outputs. The algorithm does not depend on the variation of pixel intensity in the images but only depends on the relative variation unlike many standard algorithms. On a dataset of over 40 subjects with high-resolution thermographic images, we are able to extract the vessels accurately with elimination of diffused heat regions. Future studies would involve extracting features from the detected vessels and using these features for classification of malignancy.