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Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.

X-ray detectors have numerous applications in medical imaging, industrial inspection, and crystal structure analysis. Gallium oxide (Ga[sub.2]O[sub.3]) shows potential as a material for high-performance X-ray detectors due to its wide bandgap, relatively high mass attenuation coefficient, and resistance to radiation damage. In this study, we present Sn-doped Ga[sub.2]O[sub.3] microwire detectors for solar-blind and X-ray detection. The developed detectors exhibit a switching ratio of 1.66 × 10[sup.2] under X-ray irradiation and can operate stably from room temperature to 623 K, which is one of the highest reported operating temperatures for Ga[sub.2]O[sub.3] X-ray detectors to date. These findings offer a promising new direction for the design of Ga[sub.2]O[sub.3]-based X-ray detectors.

Herein, a novel Pt anchoring WO.sub.3 nanoarray catalyst is reported and its application as HzOR and HER bifunctional electrocatalysts which shown to have superior bifunctional performance for HzOR/HER under alkaline conditions. The Pt/WO.sub.3/NF requires an ultrasmall overpotential of 34.6 and 226.38 mV to achieve 10 and 100 mA cm.sup.-2 for the hydrazine oxidation reaction (HzOR), respectively. When used for HER-HzOR electrolysis, Pt/WO.sub.3/NF requires low voltages of 0.717 V and 1.049 V at 100 and 200 mA cm.sup.-2, respectively.

Malignant peripheral nerve sheath tumor (MPNST) is a rare and highly aggressive soft tissue tumor. The appearance of MPNSTs is often similar to that of other soft tissue tumors, making diagnosis difficult. Additionally, the lack of specific criteria and guidelines for establishing the diagnosis of MPNST makes it very challenging to diagnose. Radiological examinations can help confirm the diagnosis. In this case report, we present multimodal radiological findings used in diagnosing MPNST. Therefore, understanding the features and characteristics of MPNST findings in various radiological modalities is crucial for accurate diagnosis. Early identification through radiological examinations can facilitate prompt management and improved patient outcomes.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

Large-sized, room-temperature ultrafast scintillator single crystals are highly demanded for fast timing applications such as time of flight–positron emission tomography, high-speed medical imaging, and pulse heavy-ray detection. Sub-nanosecond scintillation was discovered in 16 mm sized CsPbCl[sub.3]Br[sub.x] single crystals in our previous research. In this work, the crystal size of CsPbCl[sub.3]Br[sub.0.03] was enlarged to 2 inches (50.8 mm). Meanwhile, by precisely optimizing the vertical Bridgman growth process, we further increased the concentration of Br dopant to realize even faster scintillation decay. In this study, we conducted a series of tests on the grown crystals, including temperature-dependent photoluminescence tests, alpha particle excitation tests, X-ray imaging tests, etc. Via the strategy of the incorporation of Br[sub.2], Br dopant introduces highly efficient fast recombination centers in perovskite CsPbCl[sub.3]Br[sub.0.03] crystals, resulting in an unprecedently fast scintillation decay time of 303 ps under [sup.241]Am α-particle excitation, which is significantly shorter than that of the pure CsPbCl[sub.3] and all other perovskites by at least two orders of magnitude. Benefiting from the excellent optical transparency and high crystalline quality of the CsPbCl[sub.3]Br[sub.0.03] crystal, an X-ray spatial resolution of up to 20 lp/mm is achieved. These results further demonstrate the great potential of large-sized CsPbCl[sub.3]Br[sub.x] single crystals for fast timing applications.

Alzheimer’s disease is the most common form of dementia and the fifth-leading cause of death among people over the age of 65. In addition, based on official records, cases of death from Alzheimer’s disease have increased significantly. Hence, early diagnosis of Alzheimer’s disease can increase patients’ survival rates. Machine learning methods on magnetic resonance imaging have been used in the diagnosis of Alzheimer’s disease to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on MRI images is complicated, requiring the involvement of an expert user. Therefore, implementing deep learning as an automatic feature extraction method could minimize the need for feature extraction and automate the process. In this study, we propose a pre-trained CNN deep learning model ResNet50 as an automatic feature extraction method for diagnosing Alzheimer’s disease using MRI images. Then, the performance of a CNN with conventional Softmax, SVM, and RF evaluated using different metric measures such as accuracy. The result showed that our model outperformed other state-of-the-art models by achieving the higher accuracy, with an accuracy range of 85.7% to 99% for models with MRI ADNI dataset.

Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors. Existing learning-based fusion approaches attempt to construct various loss functions to preserve complementary features, while neglecting to discover the inter-relationship between the two modalities, leading to redundant or even invalid information on the fusion results. Moreover, most methods focus on strengthening the network with an increase in depth while neglecting the importance of feature transmission, causing vital information degeneration. To alleviate these issues, we propose a coupled contrastive learning network, dubbed CoCoNet, to realize infrared and visible image fusion in an end-to-end manner. Concretely, to simultaneously retain typical features from both modalities and to avoid artifacts emerging on the fused result, we develop a coupled contrastive constraint in our loss function. In a fused image, its foreground target/background detail part is pulled close to the infrared/visible source and pushed far away from the visible/infrared source in the representation space. We further exploit image characteristics to provide data-sensitive weights, allowing our loss function to build a more reliable relationship with source images. A multi-level attention module is established to learn rich hierarchical feature representation and to comprehensively transfer features in the fusion process. We also apply the proposed CoCoNet on medical image fusion of different types, e.g., magnetic resonance image, positron emission tomography image, and single photon emission computed tomography image. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) performance under both subjective and objective evaluation, especially in preserving prominent targets and recovering vital textural details.

Background Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. Methods 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. Results The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. Conclusion The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.

Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed. A primer for deep-learning techniques for healthcare, centering on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods.