了解伴随衰老过程的内部生理变化是医学图像解释的重要方面，预期的变化在报告异常发现时起着基线的作用。最近已经证明了深度学习可以准确地从胸部X射线检查患者年龄，并显示出作为健康指标和死亡率预测因素的潜力。在本文中，我们介绍了一项关于放射科医生与最先进的深度学习模型的相对性能的新型比较研究：（a）单个胸部X射线的患者年龄估计，以及（b）排名同一患者的两个时间分离图像。我们使用一个具有1.8m胸部X射线的异质数据库培训模型，其地面真相患者年龄，并研究了有限的培训数据和图像分辨率对模型准确性的限制，并在公共数据上证明了概括性的性能。为了探索模型与人类之间在这些年龄预测任务上的较大性能差距，与文献中看到的其他放射学报告任务相比，我们将我们的年龄预测模型纳入有条件的生成对抗网络（CGAN），允许可视化确定的语义特征通过预测模型对年龄预测很重要，将确定的特征与临床医生依赖的特征进行比较。

Medical image segmentation (MIS) is essential for supporting disease diagnosis and treatment effect assessment. Despite considerable advances in artificial intelligence (AI) for MIS, clinicians remain skeptical of its utility, maintaining low confidence in such black box systems, with this problem being exacerbated by low generalization for out-of-distribution (OOD) data. To move towards effective clinical utilization, we propose a foundation model named EvidenceCap, which makes the box transparent in a quantifiable way by uncertainty estimation. EvidenceCap not only makes AI visible in regions of uncertainty and OOD data, but also enhances the reliability, robustness, and computational efficiency of MIS. Uncertainty is modeled explicitly through subjective logic theory to gather strong evidence from features. We show the effectiveness of EvidenceCap in three segmentation datasets and apply it to the clinic. Our work sheds light on clinical safe applications and explainable AI, and can contribute towards trustworthiness in the medical domain.

Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for Land Use classification. The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost. We use a Federated UNet model for Semantic Segmentation of satellite and street view images. The novelty of the proposed architecture is the integration of Knowledge Distillation to reduce communication cost and response time. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street View and satellite images respectively. Our proposed framework has the potential to be a game-changer in real-time tracking of climate change across the planet.

Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retinal edema lesions joint segmentation from OCT images and enabling the segmentation results more reliable. In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment. Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed. Meanwhile, to make the segmentation results more reliable, a novel uncertainty segmentation head based on the subjective logical evidential theory is introduced to generate the final segmentation results with a corresponding overall uncertainty evaluation score map. We conduct comprehensive experiments on the public database of AI-Challenge 2018 for retinal edema lesions segmentation, and the results show that our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches. The code will be released on: https://github.com/LooKing9218/ReliableRESeg.

Accurately predicting interactive road agents' future trajectories and planning a socially compliant and human-like trajectory accordingly are important for autonomous vehicles. In this paper, we propose a planning-centric prediction neural network, which takes surrounding agents' historical states and map context information as input, and outputs the joint multi-modal prediction trajectories for surrounding agents, as well as a sequence of control commands for the ego vehicle by imitation learning. An agent-agent interaction module along the time axis is proposed in our network architecture to better comprehend the relationship among all the other intelligent agents on the road. To incorporate the map's topological information, a Dynamic Graph Convolutional Neural Network (DGCNN) is employed to process the road network topology. Besides, the whole architecture can serve as a backbone for the Differentiable Integrated motion Prediction with Planning (DIPP) method by providing accurate prediction results and initial planning commands. Experiments are conducted on real-world datasets to demonstrate the improvements made by our proposed method in both planning and prediction accuracy compared to the previous state-of-the-art methods.

Spiking neural networks (SNN) are a viable alternative to conventional artificial neural networks when energy efficiency and computational complexity are of importance. A major advantage of SNNs is their binary information transfer through spike trains. The training of SNN has, however, been a challenge, since neuron models are non-differentiable and traditional gradient-based backpropagation algorithms cannot be applied directly. Furthermore, spike-timing-dependent plasticity (STDP), albeit being a spike-based learning rule, updates weights locally and does not optimize for the output error of the network. We present desire backpropagation, a method to derive the desired spike activity of neurons from the output error. The loss function can then be evaluated locally for every neuron. Incorporating the desire values into the STDP weight update leads to global error minimization and increasing classification accuracy. At the same time, the neuron dynamics and computational efficiency of STDP are maintained, making it a spike-based supervised learning rule. We trained three-layer networks to classify MNIST and Fashion-MNIST images and reached an accuracy of 98.41% and 87.56%, respectively. Furthermore, we show that desire backpropagation is computationally less complex than backpropagation in traditional neural networks.

行星漫游者任务必须利用基于机器学习的感知来继续发生地球外探索，几乎没有人类的存在。火星地形细分对于漫游车导航和避免危害至关重要，以执行进一步的探索性任务，例如土壤样品收集和寻找有机化合物。当前的火星地形细分模型需要大量标记的数据才能实现可接受的性能，还需要重新培训以在不同域中的部署，即不同的漫游者任务或不同的任务，即地质识别和导航。这项研究提出了一种半监督的学习方法，该方法利用了骨干的无监督对比度预处理，用于对火星表面的多效率语义分割。该模型将通过使用混合域训练套件来确保具有多样性的混合域训练套件，从而扩展到当前的火星分割能力，以便在不同的火星漫游者任务中部署以进行地形导航。使用平均像素精度的评估结果表明，与单个领域训练和监督培训相比，半监督的混合域方法通过达到火星科学实验室的好奇心漫游者的精度为97％，MARS 2020 Perseverance Perseverance Rover提高了精度。 。此外，使用召回度量与标准的跨透镜损失相比，使用召回度量的损失功能提供不同的权重方法将对少数族裔或稀有类别的模型提高了30％以上。这些结果可以以数据效率的方式为Rover任务提供未来的多任务和多任务语义细分。

个性化移动代理中的感知系统需要开发室内场景理解模型，这些模型可以理解3D几何，捕获客观性，分析人类行为等。但是，与户外环境的模型相比，该方向并未得到充分探索（例如自动驾驶系统，包括行人预测，汽车检测，交通标志识别等）。在本文中，我们首先讨论主要挑战：不足，甚至没有标记为现实世界室内环境的数据，以及其他挑战，例如异质信息来源（例如RGB图像和LIDAR点云）之间的融合，建模关系建模关系在各种输出集（例如3D对象位置，深度估计和人类姿势）和计算效率之间。然后，我们描述MMISM（多模式输入多任务输出室内场景理解模型）来应对上述挑战。 MMISM认为RGB图像以及稀疏的LIDAR点是输入和3D对象检测，深度完成，人体姿势估计和语义分割作为输出任务。我们表明，MMISM在PAR上执行甚至比单任务模型更好。例如，我们在基准Arkitscenes数据集上将基线3D对象检测结果提高了11.7％。

Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localization highly depends on the context formed by their surrounding areas. In addition, the required precision is usually higher than segmentation and object detection tasks. Therefore, localization has its unique challenges different from segmentation or detection. In this paper, we propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to learn the contextualized features in different scales. Then, an attentive fusion module is adopted to aggregate multi-scale features, which consists of 1) a co-attention network with a multiple regions-of-interest (ROIs) scheme that learns complementary features from the multiple ROIs, 2) an attention-based fusion module which integrates the multi-ROIs features and non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the fovea localization in fundus images and scleral spur localization in AS-OCT images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods. The source code and trained models of ZIAN are available at https://github.com/leixiaofeng-astar/OMIA9-ZIAN.

用户POI矩阵的稀疏性是下一个POI推荐的一个确定的问题，它阻碍了对用户偏好的有效学习。为了关注问题的更详细的扩展，我们为下一个新的（$ n^2 $）POI推荐任务提出了联合三胞胎损失学习（JTLL）模块，这更具挑战性。我们的JTLL模块首先从用户的历史POI访问序列中计算出其他培训样本，然后，提出了设计的三重态损耗功能，以根据其各自的关系减少POI和用户嵌入的距离。接下来，JTLL模块将与最近的方法共同培训，以学习推荐任务的未访问关系。在两个已知的实际LBSN数据集上进行的实验表明，我们的联合培训模块能够改善最近现有作品的性能。