With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

It is well known in Traditional Chinese Medicine (TCM) that a person's wrist pulse signal can reflect their health condition. Recently, many computerized wrist pulse AI systems have been proposed to simulate a practitioner's three fingers in order to acquire the wrist pulse signals (three positions/channels) from a candidate's wrist dynamically, before evaluating their health status based on the various feature extraction and detection methods. However, few works have investigated the correlation of the extracted features from the three wrist channels and comprehensively fused the various features together, which can improve the performance of wrist pulse diagnosis. In this paper, we propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. In detail, two different sensors, i.e., pressure and photoelectricity are used to capture the three channels of the wrist pulse signals. These are used to generate two different features by applying the stacked sparse autoencoder and wavelet scattering. Each feature of one wrist pulse sample is regarded as a node associated with its corresponding feature vector, and used to construct a graph for one candidate. A novel algorithm is implemented to construct different graphs for different candidates, which are used for wrist pulse diagnosis by developing graph convolutional networks. Experimental results indicate that our proposed AI-based method can obtain superior performances compared to other state-of-the-art approaches.

The epicardial fat plays a key role in the development of many cardiovascular diseases. It is necessary and useful to precisely segment this fat from CT scans in clinical studies. However, it is not feasible to manually segment this fat in clinical practice, as the workload and cost for technicians or physicians is quite high. In this work, we propose a novel method for automatic segmentation and quantification of epicardial fat from CT scans accurately. In detail, dual U-Nets with the morphological processing layer is used for this goal. The first network is based on the U-Net framework to detect the pericardium, before segmenting its inside region. A morphological layer is concatenated as the following layer of the first network, to refine and obtain the ideal inside region of the pericardium. While the second network is also applied using U-Net as its backbone to find and segment the epicardial fat of the processed inside region from the pericardium using the first network. Our proposed method obtains the highest mean Dice similarity (91.19%), correlation coefficient (0.9304) compared to other state-of-art methods on a cardiac CT dataset with 20 patients. The results indicate our proposed method is effective for quantifying epicardial fat automatically.

Adversarial attacks aimed at subverting recognition systems have laid bare significant security vulnerabilities inherent in deep neural networks. In the automatic speech recognition (ASR) domain, prevailing defense mechanisms have primarily centered on pre-processing procedures to mitigate adversarial threats stemming from such attacks. However, despite their initial success, these methods have shown surprising vulnerabilities when confronted with robust and adaptive adversarial attacks. This paper proposes an adaptive unified defense framework tailored to address the challenges posed by robust audio adversarial examples. The framework comprises two pivotal components: (1) a unified pre-processing mechanism is designed to disrupt the continuity and transferability of adversarial attacks. Its objective is to thwart the consistent operation of adversarial examples across different systems or conditions, thereby enhancing the robustness of the defense. (2) an adaptive ASR transcription method is proposed to further bolster our defense strategy. Empirical experiments conducted using two benchmark audio datasets within a state-of-the-art ASR system affirm the effectiveness of our adaptive defense framework. It achieves an impressive 100% accuracy rate against representative audio attacks and consistently outperforms other state-of-the-art defense techniques, achieving an accuracy rate of 98.5% even when faced with various challenging adaptive adversarial attacks.

The continuous advancement of shipbuilding technology has enabled the further development of sea transport and naval vessels, which also poses significant challenges to navigation safety. Accurate recognition of different types of ships is crucial. In this study, three typical ships, namely a destroyer, cruise ship, and aircraft carrier, were taken as examples. The micro-Doppler effect of these ships was analyzed using a unmanned aerial vehicle (UAV)-based ultrawideband (UWB) radar. Echo signal models were established and compared with measured Doppler signals. Furthermore, a lightweight self-supervised learning network was proposed to achieve accurate recognition of small-sample labeled ship targets. The neural network architecture, including specific layers and parameters, was introduced. To enhance the micro-Doppler features, the 1-D signals were transformed into 2-D images, thereby converting the ship recognition problem into an image classification problem. The experimental results demonstrate that the proposed network achieves high recognition accuracy under different situations. When evaluated on measured data, the overall recognition rate reaches 97.50%. Moreover, even in challenging scenarios with a low signal-to-clutter ratio (SCR) of −10 dB, the overall recognition rate for simulated data still exceeds 90%, demonstrating the effectiveness and advantages of the proposed network.