This work explores barely-supervised brain tumor segmentation where minimal supervision,i.e., fewer than ten labeled samples, is available. Current methods often neglect two key problems in barely-supervised segmentation: i) the insufficient labeled data may be not able to offer enough information to networks for accurately segmenting tumor areas across various cases; ii) networks might overfit to the relation of multiple modalities of the limited labeled data, thus overly depending on certain modalities while overlooking other valuable modalities during segmentation. To tackle these two problems, we propose a barely-supervised training framework, called BarelySAM. BarelySAM first employs Segment Anything Model (SAM) during training by generating pseudo labels for unlabeled data. In this manner, pre-trained knowledge exhibited in SAM can be exploited to compensate for limited knowledge in labeled data, boosting network training and thus improving performance. For the overfitting problem, Multi-modality Dependency Minimization (MDM) is designed in BarelySAM to construct various partial combinations for full-modal samples, thus enforcing networks to exploit each modality effectively. Experimental results on two benchmark datasets validate the effectiveness of the integrated SAM and the designed MDM module. In particular, our method attains a 89.92% Dice score for whole tumor segmentation on BRATS2020 with just 6 (2%) labeled samples, just 1.09% lower than the performance of a fully supervised approach. Besides, experiments on barely-supervised multi-modal brain tumor segmentation also validate that our method is inherently robust against missing modalities.

Prominent solutions for medical image segmentation are typically tailored for automatic or interactive setups, posing challenges in facilitating progress achieved in one task to another. This also necessitates separate models for each task, duplicating both training time and parameters. To address above issues, we introduce S2VNet, a universal framework that leverages Slice-to- Volume propagation to unify auto-matic/interactive segmentation within a single model and one training session. Inspired by clustering-based segmen-tation techniques, S2VNet makes full use of the slice-wise structure of volumetric data by initializing cluster centers from the cluster results of previous slice. This enables knowl-edge acquired from prior slices to assist in the segmentation of the current slice, further efficiently bridging the communication between remote slices using mere 2D networks. Moreover, such a framework readily accommodates inter-active segmentation with no architectural change, simply by initializing centroids from user inputs. S2VNet distin-guishes itselfby swift inference speeds and reduced memory consumption compared to prevailing 3D solutions. It can also handle multi-class interactions with each of them serving to initialize different centroids. Experiments on three benchmarks demonstrate S2VNet surpasses task-specified solutions on both automatic/interactive setups.

Difficulty adjustment algorithm (DAA) is critical for PoW blockchain systems in currency issuance, transaction throughput, and blockchain security. Most DAAs concentrate on the unique goal of block-making stability from seconds to minutes. In contrast, we propose a new DAA with the goal of stabilizing orphan rates among the blocks made recently, observing that the orphan rate is more indicative since too high orphan rate indicates poor blockchain security and waste of honest hash power whereas too low orphan rate indicates poor blockchain throughout and wast of time awaiting other blocks. We notice a security defect of the recent orphan-rate DAA (NDSS 2022) in which an attacker may continually create orphan blocks and mislead miners to wrongly increase the difficulty. To fix this defect, our enhanced DAA takes into the orphan rate variation during the historical slots with smooth exponential decay affection on current difficulty adjustment. Theoretical and experimental analyses show that our DAA is effective and secure.

Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations (including shapes and intensities) of different unlabeled MRI images with respect to the atlas via 3D variational autoencoders (VAEs). In this manner, our method is able to exploit the learned distributions of image deformations to generate new authentic brain MRI images, and the number of generated samples will be sufficient to train a deep segmentation network. Furthermore, we introduce a new standard segmentation benchmark to evaluate the generalization performance of a segmentation network through a cross-dataset setting (collected from different sources). Extensive experiments demonstrate that our method outperforms the state-of-the-art one-shot medical segmentation methods. Our code has been released at https://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data.

Most existing brain tumor segmentation methods usually exploit multi-modal magnetic resonance imaging (MRI) images to achieve high segmentation performance. However, the problem of missing certain modality images often happens in clinical practice, thus leading to severe segmentation performance degradation. In this work, we propose a Region-aware Fusion Network (RFNet) that is able to exploit different combinations of multi-modal data adaptively and effectively for tumor segmentation. Considering different modalities are sensitive to different brain tumor regions, we design a Region-aware Fusion Module (RFM) in RFNet to conduct modal feature fusion from available image modalities according to disparate regions. Benefiting from RFM, RFNet can adaptively segment tumor regions from an incomplete set of multi-modal images by effectively aggregating modal features. Furthermore, we also develop a segmentation-based regularizer to prevent RFNet from the insufficient and unbalanced training caused by the incomplete multi-modal data. Specifically, apart from obtaining segmentation results from fused modal features, we also segment each image modality individually from the corresponding encoded features. In this manner, each modal encoder is forced to learn discriminative features, thus improving the representation ability of the fused features. Remarkably, extensive experiments on BRATS2020, BRATS2018 and BRATS2015 datasets demonstrate that our RFNet outperforms the state-of-the-art significantly.

Filter pruning has been widely applied to neural network compression and acceleration. Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune unimportant filters. There are two major limitations to these methods. First, existing methods fail to consider the variety of filter distribution across layers. To extract features of the coarse level to the fine level, the filters of different layers have various distributions. Therefore, it is not suitable to utilize the same pruning criteria to different functional layers. Second, prevailing layer-by-layer pruning methods process each layer independently and sequentially, failing to consider that all the layers in the network collaboratively make the final prediction. In this paper, we propose Learning Filter Pruning Criteria (LFPC) to solve the above problems. Specifically, we develop a differentiable pruning criteria sampler. This sampler is learnable and optimized by the validation loss of the pruned network obtained from the sampled criteria. In this way, we could adaptively select the appropriate pruning criteria for different functional layers. Besides, when evaluating the sampled criteria, LFPC comprehensively consider the contribution of all the layers at the same time. Experiments validate our approach on three image classification benchmarks. Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss.

Due to domain bias, directly deploying a deep person re-identification (re-ID) model trained on one dataset often achieves considerably poor accuracy on another dataset. In this article, we propose an Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner. Specifically, in the target domain, the re-ID model is inducted to (1) maximize distances between all person images and (2) minimize distances between similar person images. In the first case, by treating each person image as an individual class, a non-parametric classifier with a feature memory is exploited to encourage person images to move far away from each other. In the second case, according to a similarity threshold, our method adaptively selects neighborhoods for each person image in the feature space. By treating these similar person images as the same class, the non-parametric classifier forces them to stay closer. However, a problem of the adaptive selection is that, when an image has too many neighborhoods, it is more likely to attract other images as its neighborhoods. As a result, a minority of images may select a large number of neighborhoods while a majority of images has only a few neighborhoods. To address this issue, we additionally integrate a balance strategy into the adaptive selection. We evaluate our methods with two protocols. The first one is called “target-only re-ID”, in which only the unlabeled target data is used for training. The second one is called “domain adaptive re-ID”, in which both the source data and the target data are used during training. Experimental results on large-scale re-ID datasets demonstrate the effectiveness of our method. Our code has been released at https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification.

Persons are often occluded by various obstacles in person retrieval scenarios. Previous person re-identification (re-id) methods, either overlook this issue or resolve it based on an extreme assumption. To alleviate the occlusion problem, we propose to detect the occluded regions, and explicitly exclude those regions during feature generation and matching. In this paper, we introduce a novel method named Pose-Guided Feature Alignment (PGFA), exploiting pose landmarks to disentangle the useful information from the occlusion noise. During the feature constructing stage, our method utilizes human landmarks to generate attention maps. The generated attention maps indicate if a specific body part is occluded and guide our model to attend to the non-occluded regions. During matching, we explicitly partition the global feature into parts and use the pose landmarks to indicate which partial features belonging to the target person. Only the visible regions are utilized for the retrieval. Besides, we construct a large-scale dataset for the Occluded Person Re-ID problem, namely Occluded-DukeMTMC, which is by far the largest dataset for the Occlusion Person Re-ID. Extensive experiments are conducted on our constructed occluded re-id dataset, two partial re-id datasets, and two commonly used holistic re-id datasets. Our method largely outperforms existing person re-id methods on three occlusion datasets, while remains top performance on two holistic datasets.

Chaotic pulse width modulation (PWM) is introduced to Boost converters to suppress its electromagnetic interference. However, the conventional stability criterion with linear modeling method is not accurate and unpractical for chaotic PWM Boost converters because of the chaotic dynamic characteristics and the switching frequency variation. In this paper, the describing function method is applied to model the switching process under chaotic PWM in Boost converters with considering the chaotic switching frequency. Further, based on the derived describing function, the accurate stability analysis criterion of chaotic PWM Boost converters can be obtained, the stable region is a little smaller compared with the conventional PWM with fixed switching frequency. Finally, the simulation and experiment results demonstrate the accuracy and effectiveness of the proposed describing function method based on a Boost converter. Therefore, this paper provides a stability analysis method for Boost converters under chaotic PWM.