Ke Chen’s research while affiliated with Huazhong University of Science and Technology and other places

Background One key focus of partial nephrectomy is preserving renal function. Segmental renal artery occlusion with microdissection at the renal hilum confines ischemia, effectively reducing warm ischemic injury. Ultrasound-Guided Renal Artery Balloon Catheter Occluded Hybrid Partial Nephrectomy (UBo-HPN) can achieve branch occlusion without the need for dissecting the renal hilum. Objective To investigate the feasibility and safety of UBo-HPN of branch renal artery occlusion in the treatment of localized renal tumors. Subject and methods A prospective single-arm analysis involving 20 patients with renal localized tumors underwent robot assisted UBo-HPN with branch renal artery occlusion from August 2021 to July 2023, with an average follow-up of 12 months. Results All patient was successfully operated on without conversion to conventional arterial clamping or radical nephrectomy. One case (5%) of minor complication occurred in the whole cohort, which was bruising around the puncture site. The mean total operative time was 95.8 min, with a mean operative time of 21.25 min for vascular intervention. The mean warm ischemia time was 20.35 min, and the median estimated blood loss was 50 ml. The median eGFR preservation percentage at postoperative 48 h, 30 days, and the latest follow-up were 87.52%, 91.47%, and 92.2%, respectively. After a median follow-up of 10.2 (2.3–19.2) months, no patients had radiological tumor recurrence or died from tumor-related causes. Conclusions UBo-HPN with renal artery branch occlusion emerges as an efficient alternative to partial nephrectomy (PN), which achieved branch artery occlusion without dissecting the renal hilum. Long-term follow-up is expected for functional outcomes.

Recent progress of semantic point clouds analysis is largely driven by synthetic data (e.g., the ModelNet and the ShapeNet), which are typically complete, well-aligned and noisy free. Therefore, representations of those ideal synthetic point clouds have limited variations in the geometric perspective and can gain good performance on a number of 3D vision tasks such as point cloud classification. In the context of unsupervised domain adaptation (UDA), representation learning designed for synthetic point clouds can hardly capture domain invariant geometric patterns from incomplete and noisy point clouds. To address such a problem, we introduce a novel scheme for induced geometric invariance of point cloud representations across domains, via regularizing representation learning with two self-supervised geometric augmentation tasks. On one hand, a novel pretext task of predicting translation distances of augmented samples is proposed to alleviate centroid shift of point clouds due to occlusion and noises. On the other hand, we pioneer an integration of the relational self-supervised learning on geometrically-augmented point clouds in a cascade manner, utilizing the intrinsic relationship of augmented variants and other samples as extra constraints of cross-domain geometric features. Experiments on the PointDA-10 dataset demonstrate the effectiveness of the proposed method, achieving the state-of-the-art performance.

The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground facility deployments in both communication and computing domains. In this network, aerial facilities, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated edge-cloud model evolution framework, where UAVs serve as edge nodes for data collection and edge model computation. Through wireless channels, UAVs collaborate with ground cloud servers, providing cloud model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes.

Radio map is an efficient demonstration for visually displaying the wireless signal coverage within a certain region. It has been considered to be increasingly helpful for the future sixth generation (6G) of wireless networks, as wireless nodes are becoming more crowded and complicated. However, the construction of high resolution radio map is very challenging due to the sparse sampling in practical systems. Generative artificial intelligence (AI), which is capable to create synthetic data to fill in gaps in real-world measurements, is an effective technique to construct high precision radio maps. Currently, generative models for radio map construction are trained with two-dimension (2D) single band radio maps in urban scenario, which has poor generalization in diverse terrain scenarios, spectrum bands, and heights. To tackle this problem, we provide a multiband three-dimension (3D) radio map dataset with consideration of terrain and climate information, named SpectrumNet. It is the largest radio map dataset in terms of dimensions and scale, which contains the radio map of 3 spacial dimensions, 5 frequency bands, 11 terrain scenarios, and 3 climate scenarios. We introduce the parameters and settings for the SpectrumNet dataset generation, and evaluate three baseline methods for radio map construction based on the SpectrumNet dataset. Experiments show the necessity of the SpectrumNet dataset for training models with strong generalization in spacial, frequency, and scenario domains. Future works on the SpectrumNet dataset are also discussed, including the dataset expansion and calibration, as well as the extended studies on generative models for radio map construction based on the SpectrumNet dataset.

Deep learning models are the state of the art for semantic segmentation of point clouds, the success of which relies on the availability of large-scale annotated datasets. However, it can be prohibitively costly to prepare such datasets. In this work, we propose a holistic active learning (AL) approach to maximize model performance given limited annotation budgets. We investigate the appropriate sample granularity for active selection under the realistic “click” measurement of annotation cost, and demonstrate that superpoint-based selection allows for most efficient usage of the limited budget, when compared with point-level, polygon-level and instance/shape-level selection. We further propose new objective for AL acquisition function and exploit local consistency constraints to boost the performance of our superpoint-based approach. We evaluate our methods on three benchmark datasets, ShapeNet and PartNet and S3DIS. The results demonstrate that AL is an effective strategy to address the high annotation costs in semantic point cloud segmentation.

Semantic pattern of an object point cloud is determined by its topological configuration of local geometries. Learning discriminative representations can be challenging due to large shape variations of point sets in local regions and incomplete surface in a global perspective, which can be made even more severe in the context of unsupervised domain adaptation (UDA). In specific, traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries, which greatly limits their cross-domain generalization. Recently, the transformer-based models have achieved impressive performance gain in a range of image-based tasks, benefiting from its strong generalization capability and scalability stemming from capturing long range correlation across local patches. Inspired by such successes of visual transformers, we propose a novel Relational Priors Distillation (RPD) method to extract relational priors from the well-trained transformers on massive images, which can significantly empower cross-domain representations with consistent topological priors of objects. To this end, we establish a parameter-frozen pre-trained transformer module shared between 2D teacher and 3D student models, complemented by an online knowledge distillation strategy for semantically regularizing the 3D student model. Furthermore, we introduce a novel self-supervised task centered on reconstructing masked point cloud patches using corresponding masked multi-view image features, thereby empowering the model with incorporating 3D geometric information. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification. The source code of this work is available at https://github.com/zou-longkun/RPD.git.

Immunogenic cell death (ICD) is a type of cell death sparking adaptive immune responses, can reshape the tumor microenvironment (TME). Exploring key ICD-related genes in bladder cancer (BLCA) could enhance personalized treatment. TCGA BLCA patients were divided into two ICD subtypes: ICD-high and ICD-low. High ICD expression linked to increased immune cell infiltration and longer survival, but with potentially suppressed immune function. The high ICD group responded better to PD1-targeted therapy. A risk-scoring model with four ICD-related genes (CALR, IL1R1, IFNB1, IFNG) was validated across TCGA, GEO datasets, and tissue samples, showing higher risk-score correlated with weaker anti-tumor immune function, more tumor-promoting elements, lower immunotherapy response rates, and shorter patient survival.This study connects ICD-related genes to BLCA prognosis and immune infiltration, offering a vital tool for personalized treatment guidance.

Towards adversarial physical attack in real world, we argue that the main challenge lies in discounting adversarial effects by changes of point density along object surface. Most of existing point-wise perturbation based attackers concern on suppressing geometric irregularities, but it remains challenging to produce adversarial shape with geometric smoothness. Adversarial attack via the isometry transformation can alleviate irregular geometries but suffer from its rotation-sensitive nature, so its impractical assumption of category-level pre-alignment on benign object point clouds cannot be relaxed. In light of this, we explore non-rigid geometric transformations for geometry-aware adversaries with a flexible density-aware transformation on the whole point sets, which can thus impose constraints of global and local surface properties when adversarially deforming points. Experiment results on publicly benchmarking ModelNet40 and ScanObjectNN datasets verify the effectiveness of our transformation-based generation algorithms for adversarial shape and physical attack against both rotation sensitive and agnostic point classifiers, significantly outperforming existing adversarial point attackers under diverse recent defenses and the state-of-the-art physical attack methods.