5 accepted papers
Large language models can memorize information that must be removed--ranging from copyright-sensitive content (e.g., book chapters) to personally identifiable information (e.g., income)--to ensure responsible and compliant behavior. Unlearning has emerged as an efficient alternative to full retraini…
The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement. Lung cancer is the leading caus…
The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive cases are considered as crucial steps towards preventing the s…
Predicting death in lung cancer patients before initiating treatment is of paramount importance as this may guide decision-making towards more aggressive or combination of different types of treatment. In this work, we propose a Multi-scale Deep learning-based Radiomics model, referred to as "MDR-SU…
According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate. Determining the type…