LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening

A new framework for privacy-preserving federated learning has been introduced, combining Vision Transformers with lightweight homomorphic encryption to enhance histopathology classification across multiple healthcare institutions. This approach addresses the challenges posed by privacy regulations like HIPAA, which restrict direct patient data sharing, while still enabling collaborative machine learning.

A novel deep learning framework named LMLCC-Net has been introduced for predicting the malignancy of lung nodules in CT scans, utilizing Hounsfield Unit-based intensity filtering. This semi-supervised model enhances the classification of nodules by analyzing their intensity profiles and textures, which have not been fully explored in previous studies.

A hybrid transformer convolutional network has been developed to automate the segmentation of cardiac substructures in lung and breast cancer patients using both contrast-enhanced and non-contrast CT scans. This model was trained on a diverse dataset and evaluated for accuracy against established benchmarks, demonstrating its effectiveness across varying imaging conditions.

A new approach utilizing artificial intelligence (AI) is transforming lung cancer staging by enhancing the accuracy and reliability of tumor identification and measurement through advanced image segmentation techniques. This hybrid method combines deep learning with clinical knowledge to provide a more precise assessment of lung tumors, addressing the critical issue of misdiagnosis in cancer treatment.

A recent study has optimized Deterministic Lateral Displacement (DLD) devices for the label-free, size-based separation of circulating tumor cells (CTCs), particularly lung cancer cells. By employing machine learning models, including gradient boosting and random forest, the research enhances the selective isolation of these cells, which is crucial for early cancer diagnostics.

A new Explainable Cross-Disease Reasoning Framework has been proposed for assessing cardiovascular risk from low-dose chest computed tomography (LDCT) scans, which allows for a joint evaluation of lung and heart health by integrating pulmonary findings with cardiovascular implications. This framework aims to enhance interpretability in cardiopulmonary diagnostics by emulating clinical reasoning processes.