Coordinated secondary frequency regulation by flywheel energy storage arrays (FESAs) and thermal power units (TPUs) mitigates grid frequency oscillations caused by renewable integration. However, existing strategies often overlook stage-specific dynamics and multiobjective tradeoffs. To address this, a stage-aware, multiobjective, bilevel dynamic load allocation strategy is proposed based on grid assessment criteria. At the upper level, fuzzy adaptive control predicts TPU capacity, while field-based traction experiments establish precise physical power constraints for the FESA. An improved nondominated sorting genetic algorithm II (NSGA-II) with a comprehensive satisfaction model and smooth weights is developed to optimize power output across distinct regulation stages. At the lower level, a bidirectional coupling interface enables adaptive group selection, followed by an improved consensus algorithm based on the relative rate of change of state of charge (SOC) for rapid energy and state alignment. Validated with operational data from a 660-MW supercritical plant in Ningxia, China, results demonstrate superior robustness against 0.6 s network delays and 0.5 s unit failures. Compared to representative benchmarks, it increases regulation net profit by 21.83% and reduces energy losses by 19.8%, effectively balancing grid requirements with the operational safety and economic sustainability of storage-integrated plants.

Multi-object images are prevalent in various real-world scenarios, including augmented reality, advertisement design, and medical imaging. Efficient and precise editing of these images is critical for these applications. With the advent of Stable Diffusion (SD), high-quality image generation and editing have entered a new era. However, existing methods often struggle to consider each object both individually and part of the whole image editing, both of which are crucial for ensuring consistent quantity perception, resulting in suboptimal perceptual performance. To address these challenges, we propose MoEdit, an auxiliaryfree multi-object image editing framework. MoEdit facilitates high-quality multi-object image editing in terms of style transfer, object reinvention, and background regeneration, while ensuring consistent quantity perception between inputs and outputs, even with a large number of objects. To achieve this, we introduce the Feature Compensation (FeCom) module, which ensures the distinction and separability of each object attribute by minimizing the in-between interlacing. Additionally, we present the Quantity Attention (QTTN) module, which perceives and preserves quantity consistency by effective control in editing, without relying on auxiliary tools. By leveraging the SD model, MoEdit enables customized preservation and modification of specific concepts in inputs with high quality. Experimental results demonstrate that our MoEdit achieves State-Of-The-Art (SOTA) performance in multi-object image editing. Data and codes are available at https://github.com/Tear-kitty/MoEdit.

Unsupervised visual representation learning has gained significant attention in the computer vision community, driven by recent advancements in contrastive learning. Most existing contrastive learning frameworks rely on instance discrimination as a pretext task, treating each instance as a distinct category. However, this often leads to intra-class collision in a large latent space, compromising the quality of learned representations. To address this issue, we propose a novel contrastive learning method that utilizes randomly generated supervision signals. Our framework incorporates two projection heads: one handles conventional classification tasks, while the other employs a random algorithm to generate fixed-length vectors representing different classes. The second head executes a supervised contrastive learning task based on these vectors, effectively clustering instances of the same class and increasing the separation between different classes. Our method, Contrastive Learning via Randomly Generated Supervision(CLRGS), significantly improves the quality of feature representations across various datasets and achieves state-of-the-art performance in contrastive learning tasks.

Three-dimensional organ and cancer segmentation based on multi-sequence MRI is crucial for assisting clinical diagnosis. However, current automated segmentation methods often focus on specific sequences, specific organs, and specific cancers, i.e., lack of generality. To address this issue, we propose a universal segmentation network for multi-sequence MRI (UniMRISegNet) that can segment multiple organs and cancers. UniMRISegNet features a shared encoder-decoder architecture equipped with contextual prompt generation (CPG) and prompt-conditioned dynamic convolution (PCDC) modules. The CPG module encodes sequence-specific, position-specific, and organ/cancer-specific text prompts as prior information to inform UniMRISegNet about the specific task to be executed. The PCDC module can adaptively generate model weights based on the assigned prompts, enhancing the segmentation capabilities of the UniMRISegNet for specific tasks. To mitigate discrepancies between different sequences of the same organ and capture similarities between related sequences, we design a novel loss function called Semantic-Aware Cosine Similarity Loss (SACSL), which integrates the cosine similarity of text embeddings to reconcile discrepancies and similarities between MRI sequences of the same organ. We created a large-scale annotated multi-sequence, multi-organ, and multi-cancer segmentation workflow (MSOCS), and demonstrated that our UniMRISegNet outperforms other universal networks and single-task networks on MSOCS. Furthermore, the universal weights from MSOCS can be transferred to never-before-seen downstream tasks, achieving superior performance compared to training from scratch.

The Segment Anything Model (SAM) has recently gained popularity in the filed of 3D medical segmentation for its remarkable generalization capability. However, most SAM-based 2D to 3D adaptation methods requires prompting each slice in a medical volume, which is label-consuming. How to effectively propagate the prompts on the single slice to all slices while ensuring the continuity of the entire volume boundaries is still an unresolved challenge. To address this, we propose Adjacent-Slice Correspondence-guided 3D SAM(ASC-3DSAM), a novel approach for 3D medical segmentation, which matches key points or features between adjacent slices to establish correspondences for neighboring slices, thereby propagating prompt information to the entire volume using just a single prompted slice. Specifically, we present a Slice-Correlation Module (SCM), the adjacent slice as the query is segmented using the mask information from the central slice, thereby propagating prompt information to the entire volume. In addition, a hybrid continuity loss called Continuous Boundary-aware Loss(CBL) is proposed, which takes the form of a distance metric on the space of contours and integrates gradient information of the volume boundaries to supervise the continuity of the entire volume. Our approach is evaluated on two public datasets, including AMOS and BTCV. Experimental results demonstrate that ASC-3DSAM achieves state-of-the-art (SOTA) performance, with an improvement of approximately 1.86% of Dice and 1.92(mm) of HD95 compared to the next-best method. Furthermore, our method can produce smooth closed boundaries of the entire volumes outperforming the SOTA models.

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.

Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis. Traditional and fully supervised NPC diagnosis algorithms require manual delineation of regions of interest on the gigapixel of whole slide images (WSIs), which however is laborious and often biased. In this paper, we propose a weakly supervised framework based on Tokens-to-Token Vision Transformer (WS-T2T-ViT) for accurate NPC classification with only a slide-level label. The label of tile images is inherited from their slide-level label. Specifically, WS-T2T-ViT is composed of the multi-resolution pyramid, T2T-ViT and multi-scale attention module. The multi-resolution pyramid is designed for imitating the coarse-to-fine process of manual pathological analysis to learn features from different magnification levels. The T2T module captures the local and global features to overcome the lack of global information. The multi-scale attention module improves classification performance by weighting the contributions of different granularity levels. Extensive experiments are performed on the 802-patient NPC and CAMELYON16 dataset. WS-T2T-ViT achieves an area under the receiver operating characteristic curve (AUC) of 0.989 for NPC classification on the NPC dataset. The experiment results of CAMELYON16 dataset demonstrate the robustness and generalizability of WS-T2T-ViT in WSI-level classification.

Magnetic resonance imaging (MRI) has played an important role in the rapid growth of medical imaging diagnostic technology, especially in the diagnosis and treatment of brain tumors owing to its non-invasive characteristics and superior soft tissue contrast. However, brain tumors are characterized by high non-uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature. In addition, the labeling of tumor areas is time-consuming and laborious. To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation performance is further considered. In the experiments, the Dice score of the proposed segmentation model reached 97.581%, which is 12.438% higher than that of traditional U-net, demonstrating the effective segmentation of MRI brain tumor images. In conclusion, we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Biomedical image segmentation and classification are critical components in a computer-aided diagnosis system. However, various deep convolutional neural networks are trained by a single task, ignoring the potential contribution of mutually performing multiple tasks. In this paper, we propose a cascaded unsupervised-based strategy to boost the supervised CNN framework for automated white blood cell (WBC) and skin lesion segmentation and classification, called CUSS-Net. Our proposed CUSS-Net consists of an unsupervised-based strategy (US) module, an enhanced segmentation network named E-SegNet, and a mask-guided classification network called MG-ClsNet. On the one hand, the proposed US module produces coarse masks that provide a prior localization map for the proposed E-SegNet to enhance it in locating and segmenting a target object accurately. On the other hand, the enhanced coarse masks predicted by the proposed E-SegNet are then fed into the proposed MG-ClsNet for accurate classification. Moreover, a novel cascaded dense inception module is presented to capture more high-level information. Meanwhile, we adopt a hybrid loss by combining a dice loss and a cross-entropy loss to alleviate the imbalance training problem. We evaluate our proposed CUSS-Net on three public medical image datasets. Experiments show that our proposed CUSS-Net outperforms representative state-of-the-art approaches.

Although those deep learning networks have achieved remarkable performance in various biomedical image segmentation tasks, they rely on massive labeled data for training, which is time-consuming to acquire. To alleviate this challenging issue, semi-supervised learning (SSL) has shown the potential to simultaneously leverage between abundant unlabeled samples and limited labeled data. In this work, we propose a new SSL approach for left atrial and liver tumor segmentation called DTSC-Net. First, two teacher networks and one student network are optimized together through a supervised loss and two unsupervised consistency losses. Furthermore, pseudo labels are yielded by using the first teacher network on unlabeled data. In addition, the weights of the second teacher network are an exponential moving average of the student network’s weights. Moreover, the student network learns from the two teacher networks by minimizing a supervised segmentation loss, a pseudo-labeling unsupervised consistency (PUC) loss, and a dual-teacher unsupervised consistency (DUC) loss with respect to the targets of the two teacher networks. Experiment results show that our approach achieves state-of-the-art semi-supervised segmentation performance on the LA2018 and LiTS2017 datasets.

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.

Recent studies have shown that the performance of single-image super-resolution methods can be significantly boosted by using deep convolutional neural networks. In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network. In the proposed network, the feature maps of each layer are propagated into all subsequent layers, providing an effective way to combine the low-level features and high-level features to boost the reconstruction performance. In addition, the dense skip connections in the network enable short paths to be built directly from the output to each layer, alleviating the vanishing-gradient problem of very deep networks. Moreover, deconvolution layers are integrated into the network to learn the upsampling filters and to speedup the reconstruction process. Further, the proposed method substantially reduces the number of parameters, enhancing the computational efficiency. We evaluate the proposed method using images from four benchmark datasets and set a new state of the art.

An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.

We propose a fully automatic learning-based multi-atlas approach to segment the prostate using multi-channel (T1 and T2) MR images. After affine transformation to the template space, multi-scale features are extracted and separate random forest classifiers are learnt for the prostate region from the most similar T1 and T2 atlases. The probabilities from these two classifiers (T1 and T2) are then fused to obtain a robust probabilistic atlas. Finally, using the probabilistic representation for each voxel, the multi-image graph cuts algorithm is applied on these multi-channel images simultaneously to get the final segmentation. The novelty of the proposed method lies in the use of multi-channel MR images, a decision forest learnt from only the most similar MR images, and the fusion of global and local template-based classifiers for prostate segmentation. We apply this method to a set of 107 prostate images, with 77 randomly selected images used for training and the remaining 30 images for testing. The results are compared to the radiologist's labeled ground truth using cross-validation. The best result is obtained via hybrid approach in which the global classifier trained on T1 images and local template-based classifiers trained on T2 images are fused to obtain the final probability for each voxel. Our results indicate that the proposed method is robust, capable of producing accurate segmentation automatically and most importantly, not patient-specific.

The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis.