Reliable Supervision Research Articles

Reference-then-supervision framework for infrared and visible image fusion

Infrared and visible image fusion has drawn increasing attention of researchers in recent years, wherein the complementary information between two source images is extracted to synthesize a new fusion image with richer information. Deep neural network is the latest technique to tackle various image fusion problems. However, the natural absence of ground truth makes it very challenging to optimize deep learning models. While the current methods mainly consider to uses the two source images themselves or their visual features, to provide supervision for learning, which is easy to result in the imbalance between detail preservation and brightness distribution. To boost the performance of the fusion models, it is meaningful and necessary to establish a flexible framework that can combine the advantages of existing models to produce reliable supervision for model training. In this work, we propose a novel reference-then-supervision framework, which aims to fully leverage and exploit the available favorable reference information based on the performance of existing methods and then construct high-quality reliable supervision to assist in model building. For this purpose, we design an automatic filter to produce favorable reference and devise an adaptive enhancement method to construct reliable supervision, which helps to aggregate the advantages of various existing fusion methods for yielding visually pleasing results adapting to different complex scenarios. Extensive experiments on two commonly used datasets and our built challenging test set demonstrate that our framework can greatly improve the performance of existing fusion methods. Ablation study and empirical analysis also present the efficacy of our framework design. Furthermore, the applications on downstream pedestrian detection and object tracking tasks indicate the great potential of our framework. Our code and data are publicly available at https://github.com/zhenglab/ReferenceSupervisionIVIF.

Crowd Counting Using Meta-Test-Time Adaptation.

Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the specific conditions encountered during testing. The majority of current studies concentrate on unsupervised domain adaptation. These approaches commonly perform hundreds of epochs of training iterations, requiring a sizable number of unannotated data of every new target domain apart from annotated data of the source domain. Unlike these methods, we propose a meta-test-time adaptive crowd counting approach called CrowdTTA, which integrates the concept of test-time adaptation into the meta-learning framework and makes it easier for the counting model to adapt to the unknown test distributions. To facilitate the reliable supervision signal at the pixel level, we introduce uncertainty by inserting the dropout layer into the counting model. The uncertainty is then used to generate valuable pseudo labels, serving as effective supervisory signals for adapting the model. In the context of meta-learning, one image can be regarded as one task for crowd counting. In each iteration, our approach is a dual-level optimization process. In the inner update, we employ a self-supervised consistency loss function to optimize the model so as to simulate the parameters update process that occurs during the test phase. In the outer update, we authentically update the parameters based on the image with ground truth, improving the model's performance and making the pseudo labels more accurate in the next iteration. At test time, the input image is used for adapting the model before testing the image. In comparison to various supervised learning and domain adaptation methods, our results via extensive experiments on diverse datasets showcase the general adaptive capability of our approach across datasets with varying crowd densities and scales.

3D Vessel Segmentation With Limited Guidance of 2D Structure-Agnostic Vessel Annotations.

Delineating 3D blood vessels of various anatomical structures is essential for clinical diagnosis and treatment, however, is challenging due to complex structure variations and varied imaging conditions. Although recent supervised deep learning models have demonstrated their superior capacity in automatic 3D vessel segmentation, the reliance on expensive 3D manual annotations and limited capacity for annotation reuse among different vascular structures hinder their clinical applications. To avoid the repetitive and costly annotating process for each vascular structure and make full use of existing annotations, this paper proposes a novel 3D shape-guided local discrimination (3D-SLD) model for 3D vascular segmentation under limited guidance from public 2D vessel annotations. The primary hypothesis is that 3D vessels are composed of semantically similar voxels and often exhibit tree-shaped morphology. Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images. Secondly, the shape distribution from existing 2D structure-agnostic vessel annotations is introduced to guide the 3D vessels with the tree-shaped morphology by the adversarial shape constraint loss. Thirdly, to enhance training stability and prediction credibility, the highlighting-reviewing-summarizing (HRS) mechanism is proposed. This mechanism involves summarizing historical models to maintain temporal consistency and identifying credible pseudo labels as reliable supervision signals. Only guided by public 2D coronary artery annotations, our method achieves results comparable to SOTA barely-supervised methods in 3D cerebrovascular segmentation, and the best DSC in 3D hepatic vessel segmentation, demonstrating the effectiveness of our method.

Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

AbstractFor reliable supervision in multiphase processes, the droplet size represents a critical quality attribute and needs to be monitored. A promising approach is the use of smart image flow sensors since optical measurement is the most commonly used technique for droplet size distribution determination. For this, two different AI-based object detection methods, Mask RCNN and YOLOv4, are compared regarding their accuracy and their applicability to an emulsification flow process. Iterative optimization steps, including data diversification and adaption of training parameters, enable the models to achieve robust detection performance across varying image qualities and compositions. YOLOv4 shows better detection performances and more accurate results which leads to a wider application window than Mask RCNN in determining droplet sizes in emulsification processes. The final droplet detection model YOLOv4 with Hough Circle (HC) for feature extraction determines reliable droplet sizes across diverse datasets of liquid-liquid flow systems (disperse phase content 1–15 vol.-%, droplet size range 5–150 μm). Evaluating the adjustment of Confidence Scores (CS) ensures statistical representation of even smaller droplets. The droplet detection performance of the final YOLOv4 model is compared with a manual image processing method to validate the model in general as well as its accuracy and reliability. Since YOLOv4 in combination with Hough Circle (HC) shows an accurate and robust detection and size determination, it is applicable for online monitoring and characterization of various liquid-liquid flow processes. Graphical abstract

Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.

Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

RECIST-Induced Reliable Learning: Geometry-Driven Label Propagation for Universal Lesion Segmentation.

Automatic universal lesion segmentation (ULS) from Computed Tomography (CT) images can ease the burden of radiologists and provide a more accurate assessment than the current Response Evaluation Criteria In Solid Tumors (RECIST) guideline measurement. However, this task is underdeveloped due to the absence of large-scale pixel-wise labeled data. This paper presents a weakly-supervised learning framework to utilize the large-scale existing lesion databases in hospital Picture Archiving and Communication Systems (PACS) for ULS. Unlike previous methods to construct pseudo surrogate masks for fully supervised training through shallow interactive segmentation techniques, we propose to unearth the implicit information from RECIST annotations and thus design a unified RECIST-induced reliable learning (RiRL) framework. Particularly, we introduce a novel label generation procedure and an on-the-fly soft label propagation strategy to avoid noisy training and poor generalization problems. The former, named RECIST-induced geometric labeling, uses clinical characteristics of RECIST to preliminarily and reliably propagate the label. With the labeling process, a trimap divides the lesion slices into three regions, including certain foreground, background, and unclear regions, which consequently enables a strong and reliable supervision signal on a wide region. A topological knowledge-driven graph is built to conduct the on-the-fly label propagation for the optimal segmentation boundary to further optimize the segmentation boundary. Experimental results on a public benchmark dataset demonstrate that the proposed method surpasses the SOTA RECIST-based ULS methods by a large margin. Our approach surpasses SOTA approaches over 2.0%, 1.5%, 1.4%, and 1.6% Dice with ResNet101, ResNet50, HRNet, and ResNest50 backbones.

Self-supervised generative adversarial learning with conditional cyclical constraints towards missing traffic data imputation

Accurate traffic data imputation aims to fill in missing traffic values with observations as much as possible, which has long been a challenging task that affects its exploitation and application in intelligent transportation system. However, most available generative models lack extensive and effective conditional constraints during their training phase. Hence, the repair accuracy and scalability of these models are poor when dealing with complex missing cases. To fill this research gap, we propose a novel framework called self-supervised generative adversarial learning with conditional cyclical constraints (i.e., C3S2-GAL) to address the traffic data imputation problem. C3S2-GAL employs two self-supervised sparse autoencoders (i.e., S2-SAE) that share the same network structure as it's generator and discriminator to perform the fine-grained mapping and precise output of the input partial observations. With the help of multiple loss constraints, S2-SAE learns the shallow hidden relationships between observations and then generates the initial complete data. Moreover, we further enhance the repair capability of S2-SAE by calculating and integrating the pseudo-labels loss of implicit category information within the pre- and post-repair traffic data. Also, we introduce a cyclical consistency constraint to provide reliable supervision for observed and unobserved traffic values and adopt an iterative learning strategy to ensure that C3S2-GAL yields high-quality results. Finally, extensive experiments are conducted on the publicly available Guangzhou and Seattle-Loop traffic speed datasets to evaluate C3S2-GAL under different missing scenarios and missing rates. The comparison results, convergence analysis, sensitivity analysis and ablation study confirm the superiority of C3S2-GAL over existing state-of-the-art baseline models.

Improving adversarial robustness through a curriculum-guided reliable distillation

Adversarial distillation (AD) which combines adversarial training with knowledge distillation has become a powerful procedure to mitigate the effects of adversarial examples to deep neural networks, which aims at distilling a robust student network from a pre-trained robust teacher network. In AD, the teacher's reliability has been a crucial problem and recent work incorporates a self-distillation loss on the student into the AD framework, encouraging the student to partially trust the teacher and gradually trust itself more. However, the only key factor to control this trust level cannot be adaptive enough for different training samples, guiding the student to trust itself inappropriately and inspiring us to pursue a better way to obtain more reliable supervision. In this paper we revisit the performance variation of all training samples from the teacher to student, showing that previous work is not always adaptive with the distillation and can be further refined. Accordingly, a more effective and justified supervision, namely Curricular Adversarial Distillation (CAD), is proposed to help boost the self-distillation process. In CAD, the KL divergences between both the clean and adversarial outputs of teacher and the smoothed labels are calculated for the supervision to address the teacher's unreliability in the self-distillation. Besides, the smoothed labels follow a curriculum-style scheduler, getting smoothed to different degrees at different training stages and helping the teacher continuously be adaptive to the varying adversarial examples from student. Extensive experiments have shown the superiority of our strategy in distilling a robust student network against various attacks.

Weakly Supervised Hashing with Reconstructive Cross-modal Attention

On many popular social websites, images are usually associated with some meta-data such as textual tags, which involve semantic information relevant to the image and can be used to supervise the representation learning for image retrieval. However, these user-provided tags are usually polluted by noise, therefore the main challenge lies in mining the potential useful information from those noisy tags. Many previous works simply treat different tags equally to generate supervision, which will inevitably distract the network learning. To this end, we propose a new framework, termed as Weakly Supervised Hashing with Reconstructive Cross-modal Attention (WSHRCA), to learn compact visual-semantic representation with more reliable supervision for retrieval task. Specifically, for each image-tag pair, the weak supervision from tags is refined by cross-modal attention, which takes image feature as query to aggregate the most content-relevant tags. Therefore, tags with relevant content will be more prominent while noisy tags will be suppressed, which provides more accurate supervisory information. To improve the effectiveness of hash learning, the image embedding in WSHRCA is reconstructed from hash code, which is further optimized by cross-modal constraint and explicitly improves hash learning. The experiments on two widely-used datasets demonstrate the effectiveness of our proposed method for weakly-supervised image retrieval. The code is available at https://github.com/duyc168/weakly-supervised-hashing .

Exploit Domain-Robust Optical Flow in Domain Adaptive Video Semantic Segmentation

Domain adaptive semantic segmentation aims to exploit the pixel-level annotated samples on source domain to assist the segmentation of unlabeled samples on target domain. For such a task, the key is to construct reliable supervision signals on target domain. However, existing methods can only provide unreliable supervision signals constructed by segmentation model (SegNet) that are generally domain-sensitive. In this work, we try to find a domain-robust clue to construct more reliable supervision signals. Particularly, we experimentally observe the domain-robustness of optical flow in video tasks as it mainly represents the motion characteristics of scenes. However, optical flow cannot be directly used as supervision signals of semantic segmentation since both of them essentially represent different information. To tackle this issue, we first propose a novel Segmentation-to-Flow Module (SFM) that converts semantic segmentation maps to optical flows, named the segmentation-based flow (SF), and then propose a Segmentation-based Flow Consistency (SFC) method to impose consistency between SF and optical flow, which can implicitly supervise the training of segmentation model. The extensive experiments on two challenging benchmarks demonstrate the effectiveness of our method, and it outperforms previous state-of-the-art methods with considerable performance improvement. Our code is available at https://github.com/EdenHazardan/SFC.

Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation.

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation.

Single Person Dense Pose Estimation via Geometric Equivariance Consistency

We study the task of single person dense pose estimation. Specifically, given a human-centric image, we learn to map all human pixels onto a 3D, surface-based human body model. Existing methods approach this problem by fitting deep convolutional networks on sparse annotated points where the regression on both surface coordinate components for each body part is uncorrelated and optimized separately. In this work, we devise a novel, unified loss function that explicitly characterizes the correlation for surface coordinates regression, achieving significant improvements in both accuracy and efficiency. Furthermore, based on an observation that the image-to-surface correspondence is intrinsically invariant to geometric transformations from input images, we propose to enforce a geometric equivariance consistency on the target mapping, thereby allowing us to enable reliable supervision on large amounts of unlabeled pixels. We conduct comprehensive studies on the effectiveness of our approach using a quite simple network. Extensive experiments on the DensePose-COCO dataset show that our model achieves superior performance against previous state-of-the-art methods with much less computation complexity. We hope that our work would serve as a solid baseline for future study in the field. The code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Johnqczhang/densepose.pytorch</uri> .

Incremental Pedestrian Attribute Recognition via Dual Uncertainty-Aware Pseudo-Labeling

Incremental pedestrian attribute recognition (IncPAR) aims to learn novel person attributes continuously and avoid the catastrophic forgetting, which is an essential problem for image forensic and security applications, e.g., suspect search. Different from the conventional continual learning for visual classification, we formulate the IncPAR as a problem of multi-label continual learning with incomplete labels (MCL-IL), where the training samples in a novel task are annotated with only a few categories of interest but may implicitly contain other attributes of previous tasks. The incomplete label assignments is a challenging and frequently-encountered issue in real-world multi-label classification applications due to a number of reasons, e.g., incomplete data collection, moderate budget for annotations, etc. To tackle the MCL-IL problem, we propose a self-training based approach via dual uncertainty-aware pseudo-labeling (DUAPL) to transfer the knowledge learned in previous tasks to novel tasks. Specially, both kinds of uncertainties, i.e., aleatoric uncertainty and epistemic uncertainty, are modeled to mitigate the negative influences of noisy pseudo labels induced by low quality samples and immature models learned by inadequate training in early tasks. Based on the DUAPL, more reliable supervision signals can be estimated to prevent the model evolution from forgetting attributes seen in previous tasks. For standard evaluations of MCL-IL methods, two benchmarks on IncPAR, termed RAP-CL and PETA-CL, are constructed by re-organizing public human attribute datasets. Extensive experiments have been performed on these benchmarks to compare the proposed method with multiple baselines. The superior performance in terms of both recognition accuracies and forgetting ratios demonstrate the effectiveness of the proposed DUAPL for IncPAR.

Uncertainty-Guided Semi-Supervised Few-Shot Class-Incremental Learning With Knowledge Distillation

Class-Incremental Learning (CIL) aims at incrementally learning novel classes without forgetting old ones. This capability becomes more challenging when novel tasks contain one or a few labeled training samples, which leads to a more practical learning scenario, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., Few-Shot Class-Incremental Learning (FSCIL). The dilemma on FSCIL lies in serious overfitting and exacerbated catastrophic forgetting caused by the limited training data from novel classes. In this paper, excited by the easy accessibility of unlabeled data, we conduct a pioneering work and focus on a Semi-Supervised Few-Shot Class-Incremental Learning (Semi-FSCIL) problem, which requires the model incrementally to learn new classes from extremely limited labeled samples and a large number of unlabeled samples. To address this problem, a simple but efficient framework is first constructed based on the knowledge distillation technique to alleviate catastrophic forgetting. To efficiently mitigate the overfitting problem on novel categories with unlabeled data, uncertainty-guided semi-supervised learning is incorporated into this framework to select unlabeled samples into incremental learning sessions considering the model uncertainty. This process provides extra reliable supervision for the distillation process and contributes to better formulating the class means. Our extensive experiments on CIFAR100, miniImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.

Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels

Large-scale high-resolution land-cover mapping is a way to comprehend the Earth’s surface and resolve the ecological and resource challenges facing humanity. High-resolution (≤1 m) remotely sensed images can now be captured more easily, with wider coverage, as sensors and satellites develop. Nevertheless, the synchronous renewal of land-cover maps is still challenging when using the common land-cover mapping methods, due to the requirement for high-resolution land-cover labels. Abundant low-resolution (∼30 m) land-cover products are available for use as alternative label sources, but the resolution gap between these products and the growing volume of high-resolution imagery is a barrier yet to be overcome. In this paper, to break through this obstacle, we propose a low-to-high network (L2HNet) to automatically generate high-resolution land-cover maps from high-resolution images by taking only low-resolution land-cover products as the training labels, thus getting rid of the requirement for finely labeled samples during the large-scale map updating process. Firstly, to obtain the mapping results with rich details, we propose a resolution-preserving (RP) backbone that contains parallel multi-scale convolutional layers for extracting the high-resolution features from the images. Furthermore, to settle the label noise issue caused by the mismatched resolution, a confident area selection (CAS) module and a low-to-high (L2H) loss function, with weak and unsupervised strategies, are designed for obtaining reliable supervision information from the coarse labels. The experimental results obtained for six administrative states located in the Chesapeake Bay watershed of the United States show that L2HNet outperforms several of the state-of-the-art methods and the mainstream land-cover mapping methods in creating 1-m resolution land-cover maps by taking 30-m resolution land-cover products as training labels. As a further application, L2HNet was also adopted to produce the first 1-m resolution land-cover map with level II classification hierarchy for the entire state of Maryland in the United States, which covers an area of about 33,872 km2. The land-cover map of Maryland is publicly available at http://hipag.whu.edu.cn/L2HNet.html.

All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation.

Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for their superior performance, wherein the real labels are only utilized to supervise their paired images via supervised loss while the unlabeled images are exploited by enforcing the perturbation-based "unsupervised" consistency without explicit guidance from those real labels. However, intuitively, the expert-examined real labels contain more reliable supervision signals. Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training? To this end, we discard the previous perturbation-based consistency but absorb the essence of non-parametric prototype learning. Based on the prototypical networks, we then propose a novel cyclic prototype consistency learning (CPCL) framework, which is constructed by a labeled-to-unlabeled (L2U) prototypical forward process and an unlabeled-to-labeled (U2L) backward process. Such two processes synergistically enhance the segmentation network by encouraging morediscriminative and compact features. In this way, our framework turns previous "unsupervised" consistency into new "supervised" consistency, obtaining the "all-around real label supervision" property of our method. Extensive experiments on brain tumor segmentation from MRI and kidney segmentation from CT images show that our CPCL can effectively exploit the unlabeled data and outperform other state-of-the-art semi-supervised medical image segmentation methods.

Local-to-Global Deep Clustering on ApproximateUniform Manifold

Deep clustering usually treats the clustering assignments as supervisory signals to learn a more compact representation with deep neural networks, under the guidance of clustering-oriented losses. Nevertheless, we observe that, without reliable supervision, such losses for global clustering would destroy the locally geometric structure underlying data. In this paper, we propose a local-to-global deep clustering method based on approximate uniform manifold (LGC-AUM) to address this issue in a two-stage fashion. In the local stage, an intra-manifold preservation loss is proposed to preserve intra-manifold structures locally on basis of approximate uniform manifold, and an inter-manifold discrimination loss is for global inter-manifold structure. Thus, this stage serves to learn more discriminative structure-preserving features by reducing the correlations between different manifolds, which paves the way for the final clustering. Build off the learned features, the second stage explores a clustering loss based on approximate uniform manifold to establish stable network training for effective clustering with two auxiliary distributions. Experiments on five benchmark datasets verify the efficacy of our LGC-AUM as compared to several well-behaved clustering counterparts.

PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images

Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large-scale variation, complex background interference, and nonuniform density distribution greatly limit the counting accuracy, particularly striking in remote-sensing imagery. To mitigate the above issues, this article proposes a novel framework for dense object counting in remote-sensing images, which incorporates a pyramidal scale module (PSM) and a global context module (GCM), dubbed PSGCNet, where PSM is used to adaptively capture multi-scale information and GCM is to guide the model to select suitable scales generated from PSM. Moreover, a reliable supervision manner improved from Bayesian and counting loss (BCL) is utilized to learn the density probability and then compute the count expectation at each annotation. It can relieve nonuniform density distribution to a certain extent. Extensive experiments on four remote-sensing counting datasets demonstrate the effectiveness of the proposed method and its superiority compared with state of the arts. Additionally, experiments extended on four commonly used crowd counting datasets further validate the generalization ability of the model. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gaoguangshuai/psgcnet</uri> .

A Regulatable Data Privacy Protection Scheme for Energy Transactions Based on Consortium Blockchain

In the blockchain-based energy transaction scenario, the decentralization and transparency of the ledger will cause the users’ transaction details to be disclosed to all participants. Attackers can use data mining algorithms to obtain and analyze users’ private data, which will lead to the disclosure of transaction information. Simultaneously, it is also necessary for regulatory authorities to implement effective supervision of private data. Therefore, we propose a supervisable energy transaction data privacy protection scheme, which aims to trade off the supervision of energy transaction data by the supervisory authority and the privacy protection of transaction data. First, the concealment of the transaction amount is realized by Pedersen commitment and Bulletproof range proof. Next, the combination of ElGamal encryption and zero-knowledge proof technology ensures the authenticity of audit tickets, which allows regulators to achieve reliable supervision of the transaction privacy data without opening the commitment. Finally, the multibase decomposition method is used to improve the decryption efficiency of the supervisor. Experiments and security analysis show that the scheme can well satisfy transaction privacy and auditability.

Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound

Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method is highly demanded. However, it is challenging for current deep-learning based methods to segment ovary and follicles precisely due to ambiguous boundaries and insufficient annotations. In this paper, we propose a contrastive rendering (C-Rend) framework to segment ovary and follicles with detail-refined boundaries. Furthermore, we incorporate the proposed C-Rend with a semi-supervised learning (SSL) framework, leveraging unlabeled data for better performance. Highlights of this paper include: (1) A rendering task is performed to estimate boundary accurately via enriched feature representation learning. (2) Point-wise contrastive learning is proposed to enhance the similarity of intra-class points and contrastively decrease the similarity of inter-class points. (3) The C-Rend plays a complementary role for the SSL framework in uncertainty-aware learning, which could provide reliable supervision information and achieve superior segmentation performance. Through extensive validation on large in-house datasets with partial annotations, our method outperforms state-of-the-art methods in various evaluation metrics for both the ovary and follicles.