Pathology data, primarily consisting of slides and diagnostic reports, inherently contain knowledge that is pivotal for advancing data-driven biomedical research and clinical practice. However, the hidden and fragmented nature of this knowledge acros...
Breast cancer remains a leading cause of mortality in women, underscoring the need for timely and accurate diagnosis. This paper addresses this challenge by introducing a comprehensive explainable federated learning framework for breast cancer predic...
BACKGROUND: Physical activity plays a crucial role in maintaining health and preventing chronic diseases. However, accurately assessing the impact of sports and physical training on health improvement remains a challenge. Recent advancements in deep ...
Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning ...
Due to the diverse molecular structures of chemical compounds and their intricate biological pathways of toxicity, predicting their reproductive and developmental toxicity remains a challenge. Traditional Quantitative Structure-Activity Relationship ...
Wheat plays a vital role in Pakistan's economy and food security, making accurate yield forecasting essential for planning and resource management. Traditional approaches-such as manual field surveys and remote sensing-have been widely used, but thei...
Spatial transcriptomics is a powerful technology for high-resolution mapping of gene expression in tissue samples, enabling a molecular level understanding of tissue architecture. The acquisition entails dissecting and profiling micron-thick tissue s...
Recent advancements in computer vision and deep learning have advanced automated vegetation monitoring, creating new opportunities for invasive species management. To this end, we introduce MAVSD (Multi-Angle View Segmentation Dataset), specifically ...
Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utiliz...
Semantic segmentation involves an imminent part in the investigation of medical images, particularly in the domain of microvascular decompression, where publicly available datasets are scarce, and expert annotation is demanding. In response to this c...
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