Recently, convolutional neural networks (CNNs) have achieved state-of-the-art performance in infrared small target detection. However, the limited number of public training data restricts the performance improvement of CNN-based methods. To handle the scarcity of training data, we propose a method that can generate synthetic training data for infrared small target detection. We adopt the generative adversarial network framework where synthetic background images and infrared small targets are generated in two independent processes. In the first stage, we synthesize infrared images by transforming visible images into infrared ones. In the second stage, target masks are implanted on the transformed images. Then, the proposed intensity modulation network synthesizes realistic target objects that can be diversely generated from further image processing. Experimental results on the recent public dataset show that, when we train various detection networks using the dataset composed of both real and synthetic images, detection networks yield better performance than using real data only.

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

Three-dimensional (3D) reconstruction of capsule endoscopic images has been attempted for a long time to obtain more information on small bowel structures. Due to the limited hardware resources of capsule size and battery capacity, software approaches have been studied but have mainly exhibited inherent limitations. Recently, stereo camera-based capsule endoscopy, which can perform hardware-enabled 3D reconstruction, has been developed. We aimed to evaluate the feasibility of newly developed 3D capsule endoscopy in clinical practice. This study was a prospective, single-arm, feasibility study conducted at two university-affiliated hospitals in South Korea. Small bowel evaluation was performed using a newly developed 3D capsule endoscope for patients with obscure gastrointestinal bleeding, suspected or established Crohn’s disease, small bowel tumors, and abdominal pain of unknown origin. We assessed the technical limitations, performance, and safety of the new capsule endoscope. Thirty-one patients (20 men and 11 women; mean age: 44.5 years) were enrolled. There was no technical defect preventing adequate visualization of the small bowel. The overall completion rate was 77.4%, the detection rate was 64.5%, and there was no capsule retention. All capsule endoscopic procedures were completed uneventfully. In conclusion, newly developed 3D capsule endoscopy was safe and feasible, showing similar performance as conventional capsule endoscopy. Newly added features of 3D reconstruction and size measurement are expected to be useful in the characterization of subepithelial tumours.

Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions.