LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering

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 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.

LungEvaty, a new transformer-based deep learning model, has been introduced for predicting lung cancer risk from low-dose CT (LDCT) scans. This model processes whole lung volumes efficiently, addressing limitations of existing methods that rely on pixel-level annotations or fragmentary analysis. It achieves state-of-the-art performance using only imaging data, with an optional Anatomically Informed Attention Guidance (AIAG) loss for refinement.

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.