Reviewerly/exHarmony huggingface dataset:
Leveraging Knapsack QAOA Approach for Optimal Electric Vehicle Charging

The electric vehicle (EV) industry is currently afflicted with inefficient charging systems. Considering the growing adoption of EVs, optimization strategies for efficient charging, and overcoming constraints such as limited power supply and extended waiting times, are required. The knapsack algorithm, a classical technique that maximizes value and capacity, enables the efficient utilization of the limited available power supply while minimizing waiting times in EV charging scenarios. However, the knapsack problem is notoriously NP-hard, making it difficult to find efficient solutions classically. In this paper, we propose an approach that leverages the quantum approximation optimization algorithm (QAOA) to resolve the EV charging problem using a knapsack-based formulation. By incorporating a knapsack problem constraint into the QAOA, we overcome the limitations of the original QAOA method and provide a potential solution to the knapsack problem. We extensively evaluate and analyze the effectiveness of our approach in finding optimal EV charging solutions in both noise-free simulations and noisy real quantum devices. The proposed approach achieves impressive approximation ratios of up to 100% and 50% in noise-free and noisy environments, respectively. Even with small circuit sizes, we confirm that our approach can find optimal solutions effectively.
Brief Announcement: Efficient Probabilistic Approximations for Sign and Compare

Fully Homomorphic Encryption (FHE) is a prime candidate for designing privacy-preserving schemes due to its cryptographic security guarantees. For word-wise FHE schemes, often, the complex functions need to be approximated as low-order polynomials. Meanwhile, Artificial Neural Networks (ANN) are known for their ability to approximate arbitrary functions. This paper presents an ANN-based probabilistic polynomial approximation approach using a Perceptron with linear activation in our publicly available Python library. Our approach can be used to generate approximation polynomials with desired degree terms. We further provide third and seventh-degree approximations for univariate $$Sign(x) \in \{-1,0,1\}$$ and $$Compare(a-b) \in \{0,\frac{1}{2},1\}$$ functions in the intervals $$[-1,1]$$ and $$[-5,-5]$$ . Finally, we empirically show that our polynomials improve up to 15% accuracy over Chebyshev's.
Peer-to-peer energy trading based on a hybrid blockchain system

The emergence of distributed energy and prosumers has greatly promoted the reform of the energy market. Blockchain technology provides a very potent solution for distributed energy transactions, however, blockchain technology still develops slowly in the energy field due to the unstable performance of the imperfect system. This paper aims to narrow this gap by the following steps. First, a function-driven integration method is proposed to combine the merit of blockchain distributed ledger and traditional centralized databases. Then a hybrid energy blockchain system architecture is designed, including the blockchain layer, smart contract layer, database layer, and client layer. Further, a peer-to-peer market mechanism considering energy storage arbitrage and fully automated matchmaking is developed on the hybrid blockchain system. The case study proves the effectiveness and safety of the system. The proposed architecture of combined blockchain and database technologies effectively paves the way for constructing a more reasonable and feasible distributed energy trading platform.
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. Specifically, personalized FL methods have a tendency to overfit to local data, leading to a sharp valley in the local model and inhibiting its ability to generalize to out-of-distribution data. In this paper, we propose a novel federated model soup method (i.e., selective interpolation of model parameters) to optimize the trade-off between local and global performance. Specifically, during the federated training phase, each client maintains its own global model pool by monitoring the performance of the interpolated model between the local and global models. This allows us to alleviate overfitting and seek flat minima, which can significantly improve the model's generalization performance. We evaluate our method on retinal and pathological image classification tasks, and our proposed method achieves significant improvements for out-of-distribution generalization. Our code is available at https://github.com/ubc-tea/FedSoup .
Eye-Guided Dual-Path Network for Multi-organ Segmentation of Abdomen

Multi-organ segmentation of the abdominal region plays a vital role in clinical such as organ quantification, surgical planning, and disease diagnosis. Due to the dense distribution of abdominal organs and the close connection between each organ, the accuracy of the label is highly required. However, the dense and complex structure of abdominal organs necessitates highly professional medical expertise to manually annotate the organs, leading to significant costs in terms of time and effort. We found a cheap and easily accessible form of supervised information. Recording the areas by the eye tracker where the radiologist focuses while reading abdominal images, gaze information is able to force the network model to focus on relevant objects or features required for the segmentation task. Therefore how to effectively integrate image information with gaze information is a problem to be solved. To address this issue, we propose a novel network for abdominal multi-organ segmentation, which incorporates radiologists' gaze information to boost high-precision segmentation and weaken the demand for high-cost manual labels. Our network includes three special designs: 1) a dual-path encoder to further integrate gaze information; 2) a cross-attention transformer module (CATM) that embeds human cognitive information about the image into the network model; and 3) multi-feature skip connection (MSC), which combines spatial information during down-sampling to offset the internal details of segmentation. Additionally, our network utilizes discrete wavelet transform (DWT) to further provide information on organ location and edge in different directions. Extensive experiments performed on the publicly available Synapse dataset demonstrate that our proposed method can integrate effectively gaze information and achieves Dice similarity coefficient (DSC) up to 81.87% and Hausdorff distance (HD) reduction to 11.96%, as well as gain high-quality readable visualizations. Code will be available at https://github.com/code-Porunacabeza/gaze_seg/ .
Self-supervised Learning via Inter-modal Reconstruction and Feature Projection Networks for Label-Efficient 3D-to-2D Segmentation

Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these tasks require segmentation to be performed on a subset of the input dimensions, the most common case being 3D $$\rightarrow $$ 2D. However, the performance of existing methods is strongly conditioned by the amount of labeled data available, as there is currently no data efficient method, e.g. transfer learning, that has been validated on these tasks. In this work, we propose a novel convolutional neural network (CNN) and self-supervised learning (SSL) method for label-efficient 3D $$\rightarrow $$ 2D segmentation. The CNN is composed of a 3D encoder and a 2D decoder connected by novel 3D $$\rightarrow $$ 2D blocks. The SSL method consists of reconstructing image pairs of modalities with different dimensionality. The approach has been validated in two tasks with clinical relevance: the en-face segmentation of geographic atrophy and reticular pseudodrusen in optical coherence tomography. Results on different datasets demonstrate that the proposed CNN significantly improves the state of the art in scenarios with limited labeled data by up to 8% in Dice score. Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.
UPCoL: Uncertainty-Informed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

Semi-supervised learning (SSL) has emerged as a promising approach for medical image segmentation, while its capacity has still been limited by the difficulty in quantifying the reliability of unlabeled data and the lack of effective strategies for exploiting unlabeled regions with ambiguous predictions. To address these issues, we propose an Uncertainty-informed Prototype Consistency Learning (UPCoL) framework, which learns fused prototype representations from labeled and unlabeled data judiciously by incorporating an entropy-based uncertainty mask. The consistency constraint enforced on prototypes leads to a more discriminative and compact prototype representation for each class, thus optimizing the distribution of hidden embeddings. We experiment with two benchmark datasets of two-class semi-supervised segmentation, left atrium and pancreas, as well as a three-class multi-center dataset of type B aortic dissection. For all three datasets, UPCoL outperforms the state-of-the-art SSL methods, demonstrating the efficacy of the uncertainty-informed prototype learning strategy (Code is available at https://github.com/VivienLu/UPCoL ).
LOTUS: Learning to Optimize Task-Based US Representations

Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring. Existing deep neural networks require a large amount of labeled data for training in order to achieve clinically acceptable performance. Yet, in ultrasound, due to characteristic properties such as speckle and clutter, it is challenging to obtain accurate segmentation boundaries, and precise pixel-wise labeling of images is highly dependent on the expertise of physicians. In contrast, CT scans have higher resolution and improved contrast, easing organ identification. In this paper, we propose a novel approach for learning to optimize task-based ultrasound image representations. Given annotated CT segmentation maps as a simulation medium, we model acoustic propagation through tissue via ray-casting to generate ultrasound training data. Our ultrasound simulator is fully differentiable and learns to optimize the parameters for generating physics-based ultrasound images guided by the downstream segmentation task. In addition, we train an image adaptation network between real and simulated images to achieve simultaneous image synthesis and automatic segmentation on US images in an end-to-end training setting. The proposed method is evaluated on aorta and vessel segmentation tasks and shows promising quantitative results. Furthermore, we also conduct qualitative results of optimized image representations on other organs.
Uncertainty and Shape-Aware Continual Test-Time Adaptation for Cross-Domain Segmentation of Medical Images

Continual test-time adaptation (CTTA) aims to continuously adapt a source-trained model to a target domain with minimal performance loss while assuming no access to the source data. Typically, source models are trained with empirical risk minimization (ERM) and assumed to perform reasonably on the target domain to allow for further adaptation. However, ERM-trained models often fail to perform adequately on a severely drifted target domain, resulting in unsatisfactory adaptation results. To tackle this issue, we propose a generalizable CTTA framework. First, we incorporate domain-invariant shape modeling into the model and train it using domain-generalization (DG) techniques, promoting target-domain adaptability regardless of the severity of the domain shift. Then, an uncertainty and shape-aware mean teacher network performs adaptation with uncertainty-weighted pseudo-labels and shape information. Lastly, small portions of the model's weights are stochastically reset to the initial domain-generalized state at each adaptation step, preventing the model from 'diving too deep' into any specific test samples. The proposed method demonstrates strong continual adaptability and outperforms its peers on three cross-domain segmentation tasks. Code is available at https://github.com/ThisGame42/CTTA .
Weakly Supervised Medical Image Segmentation Through Dense Combinations of Dense Pseudo-Labels

Annotating a large amount of medical imaging data thoroughly for training purposes can be expensive, particularly for medical image segmentation tasks. Instead, obtaining less precise scribble–like annotations is more feasible for clinicians. In this context, training semantic segmentation networks with limited-signal supervision remains a technical challenge. We present an innovative scribble-supervised approach to image segmentation via densely combining dense pseudo-labels which consists of groups of CNN– and ViT–based segmentation networks. A simple yet efficient dense collaboration scheme called Collaborative Hybrid Networks (CHNets) ensembles dense pseudo–labels to expand the dataset such that it mimics full-signal supervision. Additionally, internal consistency and external consistency training of the collaborating networks are proposed, so as to ensure that each network is beneficial to the others. This results in a significant overall improvement. Our experiments on a public MRI benchmark dataset demonstrate that our proposed approach outperforms other weakly-supervised methods on various metrics. The source code of CHNets, ten baseline methods, and dataset are available at https://github.com/ziyangwang007/CV-WSL-MIS .
Low-Dose CT Image Super-Resolution Network with Dual-Guidance Feature Distillation and Dual-Path Content Communication

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis yet suffered from spatial resolution loss and artifacts. Numerous methods have been proposed to deal with those issues, but there still exists drawbacks: (1) convolution without guidance causes essential information not highlighted; (2) features with fixed-resolution lose the attention to multi-scale information; (3) single super-resolution module fails to balance details reconstruction and noise removal. Therefore, we propose an LDCT image super-resolution network consisting of a dual-guidance feature distillation backbone for elaborate visual feature extraction, and a dual-path content communication head for artifacts-free and details-clear CT reconstruction. Specifically, the dual-guidance feature distillation backbone is composed of a dual-guidance fusion module (DGFM) and a sampling attention block (SAB). The DGFM guides the network to concentrate the feature representation of the 3D inter-slice information in the region of interest (ROI) by introducing the average CT image and segmentation mask as complements of the original LDCT input. Meanwhile, the elaborate SAB utilizes the essential multi-scale features to capture visual information more relative to edges. The dual-path reconstruction architecture introduces the denoising head before and after the super-resolution (SR) head in each path to suppress residual artifacts, respectively. Furthermore, the heads with the same function share the parameters so as to efficiently improve the reconstruction performance by reducing the amount of parameters. The experiments compared with 6 state-of-the-art methods on 2 public datasets prove the superiority of our method. The code is made available at https://github.com/neu-szy/dual-guidance_LDCT_SR .
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. The multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Moreover, we combine the reverse diffusion procedure to optimize the shallow output further and generate images highly approximate to real ones. The proposed method is compared with eleven state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. The superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible at github.com/longbai1006/LLCaps .
Structure-Decoupled Adaptive Part Alignment Network for Domain Adaptive Mitochondria Segmentation

Existing methods for unsupervised domain adaptive mitochondria segmentation perform feature alignment via adversarial learning, and achieve promising performance. However, these methods neglect the differences in structure of long-range sections. Besides, they fail to utilize the context information to merge the appropriate pixels to construct a part-level discriminator. To mitigate these limitations, we propose a Structure-decoupled Adaptive Part Alignment Network (SAPAN) including a structure decoupler and a part miner for robust mitochondria segmentation. The proposed SAPAN model enjoys several merits. First, the structure decoupler is responsible for modeling long-range section variation in structure, and decouple it from features in pursuit of domain invariance. Second, the part miner aims at absorbing the suitable pixels to aggregate diverse parts in an adaptive manner to construct part-level discriminator. Extensive experimental results on four challenging benchmarks demonstrate that our method performs favorably against state-of-the-art UDA methods.
CorSegRec: A Topology-Preserving Scheme for Extracting Fully-Connected Coronary Arteries from CT Angiography

Accurate extraction of coronary arteries from coronary computed tomography angiography (CCTA) is a prerequisite for the computer-aided diagnosis of coronary artery disease (CAD). Deep learning-based methods can achieve automatic segmentation of vasculatures, but few of them focus on the connectivity and completeness of the coronary tree. In this paper, we propose CorSegRec, a topology-preserving scheme for extracting fully-connected coronary artery, which integrates image segmentation, centerline reconnection, and geometry reconstruction. First, we employ a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we propose a regularized walk algorithm, by integrating distance, probabilities predicted by centerline classifier, and cosine similarity to reconnect centerlines. Third, we apply level-set segmentation and implicit modeling techniques to reconstruct the geometric model of the missing vessels. Experiment results on two datasets demonstrate that the proposed method outperforms other methods with better volumetric scores and higher vascular connectivity. Code will be available at https://github.com/YH-Qiu/CorSegRec .
TransNuSeg: A Lightweight Multi-task Transformer for Nuclei Segmentation

Nuclei appear small in size, yet, in real clinical practice, the global spatial information and correlation of the color or brightness contrast between nuclei and background, have been considered a crucial component for accurate nuclei segmentation. However, the field of automatic nuclei segmentation is dominated by Convolutional Neural Networks (CNNs), meanwhile, the potential of the recently prevalent Transformers has not been fully explored, which is powerful in capturing local-global correlations. To this end, we make the first attempt at a pure Transformer framework for nuclei segmentation, called TransNuSeg. Different from prior work, we decouple the challenging nuclei segmentation task into an intrinsic multi-task learning task, where a tri-decoder structure is employed for nuclei instance, nuclei edge, and clustered edge segmentation respectively. To eliminate the divergent predictions from different branches in previous work, a novel self distillation loss is introduced to explicitly impose consistency regulation between branches. Moreover, to formulate the high correlation between branches and also reduce the number of parameters, an efficient attention sharing scheme is proposed by partially sharing the self-attention heads amongst the tri-decoders. Finally, a token MLP bottleneck replaces the over-parameterized Transformer bottleneck for a further reduction in model complexity. Experiments on two datasets of different modalities, including MoNuSeg have shown that our methods can outperform state-of-the-art counterparts such as CA2.5-Net by 2–3% Dice with 30% fewer parameters. In conclusion, TransNuSeg confirms the strength of Transformer in the context of nuclei segmentation, which thus can serve as an efficient solution for real clinical practice. Code is available at https://github.com/zhenqi-he/transnuseg .
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis

Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by providing input data that is as rich as the output data, e.g., image sequence plus conditioning in $$\rightarrow $$ video out. However, clinical documentation is usually scarce and only single images are reported and stored, thus retrospective patient-specific analysis or the generation of rich training data becomes impossible with current approaches. In this paper, we extend elucidated diffusion models for video modelling to generate plausible video sequences from single images and arbitrary conditioning with clinical parameters. We explore this idea within the context of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We use the publicly available EchoNet-Dynamic dataset for all our experiments. Our image to sequence approach achieves an $$R^2$$ score of 93%, which is 38 points higher than recently proposed sequence to sequence generation methods. Code and weights are available at https://github.com/HReynaud/EchoDiffusion .