Semantic Data Augmentation Enhanced Invariant Risk Minimization for Medical Image Domain Generalization

Deep learning has achieved remarkable success in medical image classification. However, its clinical application is often hindered by data heterogeneity caused by variations in scanner vendors, imaging protocols, and operators. Approaches such as invariant risk minimization (IRM) aim to address this challenge of out-of-distribution generalization. For instance, VIRM improves upon IRM by tackling the issue of insufficient feature support overlap, demonstrating promising potential. Nonetheless, these methods face limitations in medical imaging due to the scarcity of annotated data and the inefficiency of augmentation strategies. To address these issues, we propose a novel domain-oriented direction selector to replace the random augmentation strategy used in VIRM. Our method leverages inter-domain covariance as a guider for augmentation direction, guiding data augmentation towards the target domain. This approach effectively reduces domain discrepancies and enhances generalization performance. Experiments on a multi-center diabetic retinopathy dataset demonstrate that our method outperforms state-of-the-art approaches, particularly under limited data conditions and significant domain heterogeneity.

Flow: A Modular Approach to Automated Agentic Workflow Generation

Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of Agentic workflows during execution has not been well-studied. A effective workflow adjustment is crucial, as in many real-world scenarios, the initial plan must adjust to unforeseen challenges and changing conditions in real-time to ensure the efficient execution of complex tasks. In this paper, we define workflows as an activity-on-vertex (AOV) graphs. We continuously refine the workflow by dynamically adjusting task allocations based on historical performance and previous AOV with LLM agents. To further enhance system performance, we emphasize modularity in workflow design based on measuring parallelism and dependence complexity. Our proposed multi-agent framework achieved efficient sub-task concurrent execution, goal achievement, and error tolerance. Empirical results across different practical tasks demonstrate dramatic improvements in the efficiency of multi-agent frameworks through dynamic workflow updating and modularization.

Ranked from Within: Ranking Large Multimodal Models for Visual Question Answering Without Labels

As large multimodal models (LMMs) are increasingly deployed across diverse applications, the need for adaptable, real-world model ranking has become paramount. Traditional evaluation methods are largely dataset-centric, relying on fixed, labeled datasets and supervised metrics, which are resource-intensive and may lack generalizability to novel scenarios, highlighting the importance of unsupervised ranking. In this work, we explore unsupervised model ranking for LMMs by leveraging their uncertainty signals, such as softmax probabilities. We evaluate state-of-the-art LMMs (e.g., LLaVA) across visual question answering benchmarks, analyzing how uncertainty-based metrics can reflect model performance. Our findings show that uncertainty scores derived from softmax distributions provide a robust, consistent basis for ranking models across varied tasks. This finding enables the ranking of LMMs on real-world, unlabeled data for visual question answering, providing a practical approach for selecting models across diverse domains without requiring manual annotation.

Multi-Dimensional Reconfigurable, Physically Composable Hybrid Diffractive Optical Neural Network

Diffractive optical neural networks (DONNs), leveraging free-space light wave propagation for ultra-parallel, high-efficiency computing, have emerged as promising artificial intelligence (AI) accelerators. However, their inherent lack of reconfigurability due to fixed optical structures post-fabrication hinders practical deployment in the face of dynamic AI workloads and evolving applications. To overcome this challenge, we introduce, for the first time, a multi-dimensional reconfigurable hybrid diffractive ONN system (MDR-HDONN), a physically composable architecture that unlocks a new degree of freedom and unprecedented versatility in DONNs. By leveraging full-system learnability, MDR-HDONN repurposes fixed fabricated optical hardware, achieving exponentially expanded functionality and superior task adaptability through the differentiable learning of system variables. Furthermore, MDR-HDONN adopts a hybrid optical/photonic design, combining the reconfigurability of integrated photonics with the ultra-parallelism of free-space diffractive systems. Extensive evaluations demonstrate that MDR-HDONN has digital-comparable accuracy on various task adaptations with 74x faster speed and 194x lower energy. Compared to prior DONNs, MDR-HDONN shows exponentially larger functional space with 5x faster training speed, paving the way for a new paradigm of versatile, composable, hybrid optical/photonic AI computing. We will open-source our codes.

Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via deep residual network

In this paper, channel estimation (CE) of intelligent reflecting surface aided near-field (NF) multi-user communication is investigated. Initially, the least square (LS) estimator and minimum mean square error (MMSE) estimator for the estimated channel are designed, and their mean square errors (MSEs) are derived. Subsequently, to fully harness the potential of deep residual networks (DRNs) in denoising, the above CE problem is reconceptualized as a denoising task, and a DRN-driven NF CE (DRN-NFCE) framework is proposed, and the Cram$\acute{e}$r-Rao lower bound (CRLB) is derived to serve as a benchmark for performance evaluation. In addition, to effectively capture and leverage these diverse channel features, a federated learning (FL) based global DRN-NFCE network, namely FL-DRN-NFCE, is constructed through collaborative training and joint optimization of single region DRN-NFCE (SR-DRN-NFCE) networks in different user regions. Here, users are divided into multiple regions. Correspondingly, a user region classifier based on convolutional neural network is designed to achieve the goal of matching datasets from different user regions to the corresponding SR-DRN-NFCE network. Simulation results demonstrate that the proposed FL-DRN-NFCE framework outperforms LS, MMSE, and no residual connections in terms of MSE, and the proposed FL-DRN-NFCE method has higher CE accuracy over the SR-DRN-NFCE method.

Enlarging Feature Support Overlap for Domain Generalization

Deep models often struggle with out-of-distribution (OOD) generalization, limiting their real-world applicability beyond controlled laboratory settings. Invariant risk minimization (IRM) addresses this issue by learning invariant features and minimizing the risk across different domains. Thus, it avoids the pitfalls of pseudo-invariant features and spurious causality associated with empirical risk minimization (ERM). However, according to the support overlap theorem, ERM and IRM may fail to address the OOD problem when pseudo-invariant features have insufficient support overlap. To this end, we propose a novel method to enlarge feature support overlap for domain generalization. Specifically, we introduce Bayesian random semantic data augmentation to increase sample diversity and overcome the deficiency of IRM. Experiments on several challenging OOD generalization benchmarks demonstrate that our approach surpasses existing models, delivering superior performance and robustness. The code is available at \url{https://github.com/YaoyaoZhu19/BSDG}.

MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting

In recent years, Transformers have become the de-facto architecture for long-term sequence forecasting (LTSF), but faces challenges such as quadratic complexity and permutation invariant bias. A recent model, Mamba, based on selective state space models (SSMs), has emerged as a competitive alternative to Transformer, offering comparable performance with higher throughput and linear complexity related to sequence length. In this study, we analyze the limitations of current Mamba in LTSF and propose four targeted improvements, leading to MambaTS. We first introduce variable scan along time to arrange the historical information of all the variables together. We suggest that causal convolution in Mamba is not necessary for LTSF and propose the Temporal Mamba Block (TMB). We further incorporate a dropout mechanism for selective parameters of TMB to mitigate model overfitting. Moreover, we tackle the issue of variable scan order sensitivity by introducing variable permutation training. We further propose variable-aware scan along time to dynamically discover variable relationships during training and decode the optimal variable scan order by solving the shortest path visiting all nodes problem during inference. Extensive experiments conducted on eight public datasets demonstrate that MambaTS achieves new state-of-the-art performance.

A Conditional Independence Test in the Presence of Discretization

Testing conditional independence has many applications, such as in Bayesian network learning and causal discovery. Different test methods have been proposed. However, existing methods generally can not work when only discretized observations are available. Specifically, consider $X_1$, $\tilde{X}_2$ and $X_3$ are observed variables, where $\tilde{X}_2$ is a discretization of latent variables $X_2$. Applying existing test methods to the observations of $X_1$, $\tilde{X}_2$ and $X_3$ can lead to a false conclusion about the underlying conditional independence of variables $X_1$, $X_2$ and $X_3$. Motivated by this, we propose a conditional independence test specifically designed to accommodate the presence of such discretization. To achieve this, we design the bridge equations to recover the parameter reflecting the statistical information of the underlying latent continuous variables. An appropriate test statistic and its asymptotic distribution under the null hypothesis of conditional independence have also been derived. Both theoretical results and empirical validation have been provided, demonstrating the effectiveness of our test methods.

MD-Dose: A Diffusion Model based on the Mamba for Radiotherapy Dose Prediction

Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation therapy plans. Therefore, automated prediction of dose distribution maps is significant in expediting the treatment process and providing a better starting point for developing radiation therapy plans. With the remarkable results of diffusion models in predicting high-frequency regions of dose distribution maps, dose prediction methods based on diffusion models have been extensively studied. However, existing methods mainly utilize CNNs or Transformers as denoising networks. CNNs lack the capture of global receptive fields, resulting in suboptimal prediction performance. Transformers excel in global modeling but face quadratic complexity with image size, resulting in significant computational overhead. To tackle these challenges, we introduce a novel diffusion model, MD-Dose, based on the Mamba architecture for predicting radiation therapy dose distribution in thoracic cancer patients. In the forward process, MD-Dose adds Gaussian noise to dose distribution maps to obtain pure noise images. In the backward process, MD-Dose utilizes a noise predictor based on the Mamba to predict the noise, ultimately outputting the dose distribution maps. Furthermore, We develop a Mamba encoder to extract structural information and integrate it into the noise predictor for localizing dose regions in the planning target volume (PTV) and organs at risk (OARs). Through extensive experiments on a dataset of 300 thoracic tumor patients, we showcase the superiority of MD-Dose in various metrics and time consumption.

Bayesian Random Semantic Data Augmentation for Medical Image Classification

Data augmentation is a critical regularization technique for deep neural networks, particularly in medical image classification. Popular data augmentation approaches include image transformation-based methods, generative data augmentation, and automatic data augmentation. However, these approaches encounter notable limitations: image transformation-based and automated data augmentation techniques cannot implement semantic transformations, leading to a constrained variety of augmented samples, and generative data augmentation methods are computationally expensive. In response to these challenges, we proposed Bayesian Random Semantic Data Augmentation (BRSDA), a novel, efficient, and plug-and-play semantic data augmentation method. BRSDA is motivated by a simple translation in the feature space along specific directions that can effectuate semantic transformations. When given a feature, we define its augmentable semantic magnitude as a random variable and estimate its distribution using variational Bayesian, then sample semantic magnitude and add to the randomly selected semantic direction to achieve semantic data augmentation. We demonstrate the effectiveness of BRSDA on five 2D and six 3D medical image datasets covering nine modalities. We also test BRSDA with mainstream neural network architectures, showcasing its robustness. Furthermore, combining BRSDA with other leading data augmentation methods achieves superior performance. Code is available online at \url{https://github.com/YaoyaoZhu19/BRSDA}.