Background: The use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform. Methods: In this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis. Results: In the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model’s diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006). Conclusions: The results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.

A high dynamic range (HDR) image is commonly used to reveal stereo illumination, which is crucial for generating high-quality realistic rendering effects. Compared to the high-cost HDR imaging technique, low dynamic range (LDR) imaging provides a low-cost alternative and is preferable for interactive graphics applications. However, the limited LDR pixel bit depth significantly bothers accurate illumination estimation using LDR images. The conflict between the realism and promptness of illumination estimation for realistic rendering is yet to be resolved. In this paper, an efficient method that accurately infers illuminations of real-world scenes using LDR panoramic images is proposed. It estimates multiple lighting parameters, including locations, types and intensities of light sources. In our approach, a new algorithm that extracts illuminant characteristics during the exposure attenuation process is developed to locate light sources and outline their boundaries. To better predict realistic illuminations, a new deep learning model is designed to efficiently parse complex LDR panoramas and classify detected light sources. Finally, realistic illumination intensities are calculated by recovering the inverse camera response function and extending the dynamic range of pixel values based on previously estimated parameters of light sources. The reconstructed radiance map can be used to compute high-quality image-based lighting of virtual models. Experimental results demonstrate that the proposed method is capable of efficiently and accurately computing comprehensive illuminations using LDR images. Our method can be used to produce better realistic rendering results than existing approaches.