… was to non-invasively predict lung cancer histology and develop robust deep-learning
based radiomics models to help differentiate clinically important histologic subtypes in NSCLC. …

… In this study, we aim to investigate the potential of NSCLC histology classification into AC and
… the NSCLC histology classification task, based on a radiomic analysis of the 3D tumor ROI. …

… In the future, we would ideally extend the classification to other types of less common lung
cancers (large-cell carcinoma, small-cell lung cancer) and histological subtypes of LUAD (…

… The combination of digital pathology and machine learning has the potential to support this
… application of deep learning to classify cytological preparations and histological specimens …

… Deep learning for digital pathology is hindered by the extremely high spatial resolution of
whole-slide images (WSIs). Most studies have employed patch-based methods, which often …

… histology classification models, these findings warrant further testing in larger independent
cohorts. Of note, the model was unable to detect ALK rearrangements despite the specific …

… Lung cancer accurate diagnosis is based on distinct histological patterns combined with
molecular data for personalized treatment. Precise lung cancer classification from a single H&E …

… Firstly, we implement a deep learning framework to classify input patches as LUAD, LUSC
… model to classify each WSI as lung adenocarcinoma or lung squamous cell carcinoma. This …

… to December 2018 on both feature engineering and Deep Learning (DL) algorithms, dividing
… knowledge, reviews devoted to lung cancer histology classification from CT data by means …

… In conclusion, machine learning/deep learning algorithms can be used to differentiate the
histological subtypes of NSCLC, namely, ADC and SCC, based on PET/CT images. This work …