Low-resolution face recognition suffers from domain shift due to the different resolution between a high-resolution gallery and a low-resolution probe set. Conventional methods use the pairwise correlation between high-resolution and low-resolution for the same subject, which requires label information for both gallery and probe sets. However, explicitly labeled low-resolution probe images are seldom available, and labeling them is labor-intensive. In this paper, we propose a novel unsupervised face domain transfer for robust low-resolution face recognition. By leveraging the attention mechanism, the proposed generative face augmentation reduces the domain shift at image-level, while spatial resolution adaptation generates domain-invariant and discriminant feature distributions. On public datasets, we demonstrate the complementarity between generative face augmentation at image-level and spatial resolution adaptation at feature-level. The proposed method outperforms the state-of-the-art supervised methods even though we do not use any label information of low-resolution probe set.

Objective: To build a novel classifier using an optimized 3D-convolutional neural network for predicting high-grade small bowel obstruction (HGSBO). Summary background data: Acute SBO is one of the most common acute abdominal diseases requiring urgent surgery. While artificial intelligence and abdominal computed tomography (CT) have been used to determine surgical treatment, differentiating normal cases, HGSBO requiring emergency surgery, and low-grade SBO (LGSBO) or paralytic ileus is difficult. Methods: A deep learning classifier was used to predict high-risk acute SBO patients using CT images at a tertiary hospital. Images from three groups of subjects (normal, nonsurgical, and surgical) were extracted; the dataset used in the study included 578 cases from 250 normal subjects, with 209 HGSBO and 119 LGSBO patients; over 38 000 CT images were used. Data were analyzed from 1 June 2022 to 5 February 2023. The classification performance was assessed based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Results: After fivefold cross-validation, the WideResNet classifier using dual-branch architecture with depth retention pooling achieved an accuracy of 72.6%, an area under receiver operating characteristic of 0.90, a sensitivity of 72.6%, a specificity of 86.3%, a positive predictive value of 74.1%, and a negative predictive value of 86.6% on all the test sets. Conclusions: These results show the satisfactory performance of the deep learning classifier in predicting HGSBO compared to the previous machine learning model. The novel 3D classifier with dual-branch architecture and depth retention pooling based on artificial intelligence algorithms could be a reliable screening and decision-support tool for high-risk patients with SBO.

In this paper, we introduce in-depth the analysis of CNNs and ViT architectures in medical images, with the goal of providing insights into subsequent research direction. In particular, the origins of deep neural networks should be explainable for medical images, but there has been a paucity of studies on such explainability in the aspect of deep neural network architectures. Therefore, we investigate the origin of model performance, which is the clue to explaining deep neural networks, focusing on the two most relevant architectures, such as CNNs and ViT. We give four analyses, including (1) robustness in a noisy environment, (2) consistency in translation invariance property, (3) visual recognition with obstructed images, and (4) acquired features from shape or texture so that we compare origins of CNNs and ViT that cause the differences of visual recognition performance. Furthermore, the discrepancies between medical and generic images are explored regarding such analyses. We discover that medical images, unlike generic ones, exhibit class-sensitive. Finally, we propose a straightforward ensemble method based on our analyses, demonstrating that our findings can help build follow-up studies. Our analysis code will be publicly available.