Ultrasound image guidance could display the movement of soft tissue in real time, which provides an important basis for the operation path selection of tumor for examinations and interventions such as precise localization and puncture biopsy. However, factors such as tissue deformation, patient motion, and instrument contact pose great challenges for real-time smooth localization tracking of target tissues and maintaining a suitable acoustic window. Not only does it reduce the accuracy of ultrasound examination, but also may prolong the examination time. In this paper, a real-time autonomous ultrasound robot trajectory planning framework is proposed to achieve global scanning of breast tissue and real-time local tracking of suspicious tumor targets. In addition, an ultrasound suspicious tumor target next moment motion attitude and position estimation algorithm and an acceleration-continuous online trajectory generation (ACOTG) algorithm are proposed. Experiments on breast phantom showed that the fluctuation difference of both global scanning and tracking scanning force was less than 5 N$\pm$15%, and ACOTG takes less than 1 microsecond to calculate the next moment state. The position, velocity, and acceleration of the tracking path did not change abruptly when the unknown target changed. The ultrasound image of the suspected tumor target could always be smoothly maintained in a suitable acoustic window, while the errors between the center of the suspected tumor target image and the center of the acoustic window in the horizontal direction and the depth direction were less than$\pm$1 mm and 30 mm$\pm$10%, respectively.Note to Practitioners—The motivation of this study was to solve the clinical problem of difficulty in keeping the suspected breast tumor target stably in the center of the field of view under the influence of tissue deformation, patient motion and instrument contact in ultrasound guidance. We proposed the idea of global conventional scanning and local online tracking to achieve the autonomous scanning, detecting and tracking of suspicious tumor targets within the global scope of the breast. ACOTG algorithm is proposed and implemented, which can make the ultrasound image of suspicious tumor target can be displayed stably in the center of the acoustic window. With the help of this system, the accuracy of ultrasound examination can be improved and the time of examination can be reduced. The proposed system can also be applied to ultrasound scanning of other parts of the body.

We introduce an ultrasound speckle decorrelation-based time-lagged functional ultrasound technique (tl-fUS) for the quantification of the relative changes in cerebral blood flow speed (rCBF [Formula: see text]), cerebral blood volume (rCBV) and cerebral blood flow (rCBF) during functional stimulations. Numerical simulations, phantom validations, and in vivo mouse brain experiments were performed to test the capability of tl-fUS to parse out and quantify the ratio change of these hemodynamic parameters. The blood volume change was found to be more prominent in arterioles compared to venules and the peak blood flow changes were around 2.5 times the peak blood volume change during brain activation, agreeing with previous observations in the literature. The tl-fUS shows the ability of distinguishing the relative changes of rCBFspeed, rCBV, and rCBF, which can inform specific physiological interpretations of the fUS measurements.

The quality of breast ultrasound imaging is greatly affected by the contact force of the probe, which largely requires experienced sonographers to complete the clinical examination. We propose a flexible and fully autonomous ultrasound scanning system for breast ultrasound imaging. It consists of an ultrasound machine, a dual robotic arms system, a multi-structured light system, a human–computer interaction system, and a flexible ultrasound probe clamping device (FUPCD). First, the dynamics model of the FUPCD was analyzed, and a closed-loop force control strategy was established. We then implemented an automatic scanning system. The hysteresis characteristics of the FUPCD and transient response of the force controller were experimentally verified. The system could keep the steady-state error less than ± 5% within 0.5 s. Second, the performance of the control system to maintain constant contact force at different scanning speeds (Note to Practitioners—The motivation of this study is to solve the problem of poor image reproducibility in breast ultrasound scanning, but the proposed system can also be applied to ultrasound scanning of other parts of the body. The position, direction, and contact force of the ultrasound probe affects the image quality and repeatability of the ultrasound, thereby affecting the diagnostic ability. Therefore, we propose and develop a flexible and fully autonomous breast ultrasound scanning system. We aimed to improve the autonomy and stability of the scanning process by designing end-to-end automated scanning strategies, flexible clamping devices, and closed-loop force control strategies. Among them, the fully automatic three-dimensional perception and trajectory planning can provide global fitting capabilities to different forms of breast surfaces and control the probe to maintain the best contact posture. At the same time, the fully automatic workflow reduces additional interference and reduces the complexity of the workflow. The flexible ultrasound probe clamping device improves the passive applicability to flexible tissues and reduces the resistance in the scanning process. The closed-loop force control strategy can adjust the contact force in real time. Experiments verified that the repeatability could be evaluated by contact force. A series of human–machine comparison experiments verified the advancement and effectiveness of the system. In future research, we will further solve the problem of abnormalities and repeatability of ultrasound images acquired during the scanning process by combining the multi-modal feedback of images and forces.

The misalignment between classification and localization is a significant performance improvement point for object detection. To cope with the misalignment problem, more attempts have been made to separate different tasks (e.g., Classification, Bounding Box Regression) by introducing extra heads, which emphasizes the separation of multiple tasks to cope with their variability. In this paper, we consider that both separation and crosstalk are important between classification and localization. Considering that the two types of tasks are different and have different regions and features of interest, they are in conflict with each other and therefore need to be separated. However, they also need to be fused, because classification and localization are, after all, about understanding the same object. To realize this idea, we introduce bidirectional crosstalk detection head in a systematic manner to provide a full deep cross-fusion between classification and localization. To our best knowledge, it is the first time that full bidirectional crosstalk is introduced between classification and localization for one-stage detector. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method. With a ResNet-50 backbone, our method can significantly improve the GFLV1 baseline by 2.0 AP with similar inference speed (18.5 fps vs. 18.3 fps) and further boost GFLV1 with a big margin (4.3 AP) by increasing our model size. Fair comparisons also show that the proposed head outperforms state-of-the-art heads (T-Head, DyHead) with comparable or faster inference speed under the same ATSS baseline model. With a Res2Net-DCN backbone, our model achieves 51.7 AP at single-model single-scale testing. The code and pretrained models will be made publicly available.

Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multiple noisy annotators. Numerous earlier works assume that all labels are noisy, while it is usually the case that a few trusted samples with clean labels are available. This raises the following important question: how can we effectively use a small amount of trusted data to facilitate robust classifier learning from multiple annotators? This paper proposes a data-efficient approach, calledTrustable Co-label Learning(TCL), to learn deep classifiers from multiple noisy annotators when a small set of trusted data is available. This approach follows the coupled-view learning manner, which jointly learns the data classifier and the label aggregator. It effectively uses trusted data as a guide to generate trustable soft labels (termed co-labels). A co-label learning can then be performed by alternately reannotating the pseudo labels and refining the classifiers. In addition, we further improve TCL for a special complete data case, where each instance is labeled by all annotators and the label aggregator is represented by multilayer neural networks to enhance model capacity. Extensive experiments on synthetic and real datasets clearly demonstrate the effectiveness and robustness of the proposed approach. Source code is available athttps://github.com/ShikunLi/TCL.

Proprioception and variable stiffness are two trending topics in soft robotics research. The former could endow soft robots with the ability to perceive the environment as well as their internal states without the need of dedicated sensors, while the latter could strengthen the otherwise excessive compliance, enabling soft robots for tasks which require a higher force. Both directions have been extensively reported in existing literature, achieving both concurrently was even more challenging. The major limiting factor was the limited stiffness due to the hyper elasticity of conventional soft robots, which increases the difficulties in capturing the continues deformation. In this work, we proposed an alternative approach to tackle these two challenges, a novel “tune-down” approach, combining proprioception with stiffness regulation and implemented over-constrained soft robotic joint designs to further strengthen this spirit. As a result, the soft robotic joint could achieve multi-directional proprioception, as well as variable stiffness tuning, concurrently, using merely an on-board sensor for basic pneumatic control. The concept, design, modeling, actuation/control, and experimental validation were presented in detail, demonstrating the efficacy and potential of the proposed approach.

Increasingly advanced deepfake approaches have made the detection of deepfake videos very challenging. We observe that the general deepfake videos often exhibit appearance-level temporal inconsistencies in some facial components between frames, resulting in discriminable spatiotemporal latent patterns among semantic-level feature maps. Inspired by this finding, we propose a predictive representative learning approach termed Latent Pattern Sensing to capture these semantic change characteristics for deepfake video detection. The approach cascades a CNN-based encoder, a ConvGRU-based aggregator and a single-layer binary classifier. The encoder and aggregator are pre-trained in a self-supervised manner to form the representative spatiotemporal context features. Finally, the classifier is trained to classify the context features, distinguishing fake videos from real ones. In this manner, the extracted features can simultaneously describe the latent patterns of videos across frames spatially and temporally in a unified way, leading to an effective deepfake video detector. Extensive experiments prove our approach’s effectiveness, e.g., surpassing 10 state-of-the-arts at least 7.92%@AUC on challenging Celeb-DF(v2) benchmark.