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Shaodong Ma, author of the article “Sustainable housing development in China: does financial institutions overcome the risks and challenges to sustainable housing?”, published in Technological and Economic Development of Economy, 30(3), 627–645, https://doi.org/10.3846/tede.2024.20581 informs that in the article, pp. 627–630, some references were not strongly related to the content, and required refinement. The corresponding references needed to be removed from the list of references in pp. 640–642. This has now been corrected. The author regrets the errors.

BackgroundUltra-wide-field (UWF) fundus photography represents an emerging retinal imaging technique offering a broader field of view, thus enhancing its utility in screening and diagnosing various eye diseases, notably diabetic retinopathy (DR). However, the application of computer-aided diagnosis for DR using UWF images confronts two major challenges. The first challenge arises from the limited availability of labeled UWF data, making it daunting to train diagnostic models due to the high cost associated with manual annotation of medical images. Secondly, existing models' performance requires enhancement due to the absence of prior knowledge to guide the learning process.PurposeBy leveraging extensively annotated datasets within the field, which encompass large-scale, high-quality color fundus image datasets annotated at either image-level or pixel-level, our objective is to transfer knowledge from these datasets to our target domain through unsupervised domain adaptation.MethodsOur approach presents a robust model for assessing the severity of diabetic retinopathy (DR) by leveraging unsupervised lesion-aware domain adaptation in ultra-wide-field (UWF) images. Furthermore, to harness the wealth of detailed annotations in publicly available color fundus image datasets, we integrate an adversarial lesion map generator. This generator supplements the grading model by incorporating auxiliary lesion information, drawing inspiration from the clinical methodology of evaluating DR severity by identifying and quantifying associated lesions.ResultsWe conducted both quantitative and qualitative evaluations of our proposed method. In particular, among the six representative DR grading methods, our approach achieved an accuracy (ACC) of 68.18% and a precision (pre) of 67.43%. Additionally, we conducted extensive experiments in ablation studies to validate the effectiveness of each component of our proposed method.ConclusionIn conclusion, our method not only improves the accuracy of DR grading, but also enhances the interpretability of the results, providing clinicians with a reliable DR grading scheme.

Segmentation of corneal layer interfaces in optical coherence tomography (OCT) images is important for diagnostic and surgical purposes, while manual segmentation is a time-consuming and tedious process. This paper presents a novel technique for the automatic segmentation of corneal layer interfaces using customized initial layer estimation and a gradient-based segmentation method. The proposed method was also extended to three-dimensional OCT images. Validation was performed on two corneal datasets, one with 37 B-scan images of healthy human eyes and the other with a 3D volume scan of a porcine eye. The approach showed robustness in extracting different layer boundaries in the low-SNR region with lower computational cost but higher accuracy compared to existing techniques. It achieved segmentation errors below 2.1 pixels for both the anterior and posterior layer boundaries in terms of mean unsigned surface positioning error for the first dataset and 2.6 pixels (5.2 μm) for segmenting all three layers that can be resolved in the second dataset. On average, it takes 0.7 and 0.4 seconds to process a cross-sectional B-scan image for datasets one and two, respectively. Our comparative study also showed that it outperforms state-of-the-art methods for quantifying layer interfaces in terms of accuracy and time efficiency.

Housing industry is one of the major threats to global environment and resource depletion. Particularly in China, it is regarded as a major challenge due to massive population growth. The most common barriers involve; economic barriers and environmental barriers. Therefore, to promote sustainable housing development, the management of these barriers is most crucial. This study is an attempt to overcome these barriers with the help of financial institutions in the context of China. The sample of the study are construction sector employees which are selected through simple random sampling method. Partial Least Square (PLS) is employed as statistical tool to analyze the primary data. Results revealed the positive role of financial institutions to overcome the challenges related to the economic barriers and environmental barriers. Financing from banks for the sustainable housing schemes can reduce the economic barriers and help to fulfil the sustainable housing criteria. Similarly, the environment requirements can also be achieved through environmental policy developed by the banks in China. This study recommended the Chinese government to promote sustainable hosing development through the promotion of bank financing and implementation of banks environmental policies. First published online 14 March 2024