Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction. (arXiv:2210.15297v1 [eess.IV])

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 cause of
mortality from cancer worldwide and has various histologic types, among which
LUAC has recently been the most prevalent. LUACs are classified as
pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and
accurate knowledge of the lung nodules malignancy leads to …

arxiv cae cancer fusion hybrid lung cancer prediction swin temporal transformers