BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.

Gelless Secondary Growth of Zeolitic Aluminophosphate Membranes on Porous Supports with High Performance in CO2 /CH4 Separation.

Zeolitic aluminophosphate, a three-dimensional microporous material, with an average pore size of 0.38 nm is good candidate for molecular sieve application in CO2 gas separation. The separation of CO2 /CH4 gas mixtures for precombustion processes is desirable from the standpoint of both environmental concerns and energy efficiency. This study concerns an environmentally friendly method to synthesize zeolitic aluminophosphate thin films on various configurations and low-cost kaolin porous substrates with high performance in the separation of CO2 /CH4 mixtures. The membranes are prepared by a gelless seed growth method that uses lower amounts of chemicals, forms no liquid gel, chemical waste, or byproducts and generates no washing water. The obtained membranes show very high selectivity for CO2 with a CO2 /CH4 separation factor above 1000 in the separation of CO2 /CH4 gas mixtures.