The Oncotype DX® Recurrence Score (RS) is essential for guiding adjuvant chemotherapy decisions in early-stage ER+/HER2- breast cancer, but it is costly, centralized, and not always accessible. This study developed a radiopathomic approach to estimate RS from routinely available data. A radiomic model trained on MRI using LightGBM achieved an AUC of 0.74 on an external test set, while the pathomic model using ElasticNet reached an AUC of 0.74 in cross-validation. While multimodal fusion improved discrimination in 20 paired cases (AUC $=0.74$; balanced accuracy $=0.77$). Integration with Ki67 and PGR within a nomogram further enhanced prediction (AUC $=0.76$). High specificity and NPV (0.93 and 0.88, respectively) supported the reliable identification of lowrisk patients, potentially helping reduce unnecessary chemotherapy. To our knowledge, this represents the first integrated radiopathomic signature for Oncotype DX® prediction, highlighting the feasibility of noninvasive genomic risk estimation and the potential of multiomic AI to support personalized treatment guidance in ER+/HER2breast cancer.
Automated radiology report generation requires complex compositional reasoning to translate subtle visual findings into coherent diagnostic narratives. Current models often fail to capture fine-grained pathological details, leading to generic or factually inconsistent reports. To address these limitations, we propose an expert model guided visual programming framework. It decomposes radiological reasoning into specialized subtasks. Each subtask is executed by a domain-specific expert model. We use a two-stage progressive fine-tuning strategy to fine-tune the model. This architecture enables the vision-language model to leverage the precision of specialized medical vision tools while preserving strong language generation capabilities. We show that small, task-specific models can effectively guide the training of large medical language models. Through our experiments, we show an improved state-of-the-art performance on IU-XRay and MIMIC-CXR datasets.
Deep learning has greatly advanced electron microscopy (EM) segmentation, yet supervised models remain limited by costly voxel annotations and poor generalization across datasets with large domain shifts in contrast, texture, and morphology. These discrepancies distort both appearance and organelle topology, making structure-preserving adaptation essential for reliable EM analysis. To address this challenge, we propose DiSMemNet, a unified framework for unsupervised domain adaptation (UDA) that jointly enhances appearance-structure alignment and cross-domain generalization. The framework consists of two synergistic modules: a gated contrastive translation module that preserves morphological continuity during adversarial alignment, and a distillation-memory synergy module that stabilizes pseudo-label learning via multi-scale distillation and prototype-guided refinement. Through unified optimization of semantic, structural, and appearance consistency, DiSMemNet achieves topology-preserving adaptation and stable convergence across diverse EM domains. Extensive experiments on MitoADWT, SynADWT, and Lucchi datasets demonstrate that DiSMemNet achieves state-of-the-art performance, yielding sharper boundaries, continuous structures, and strong generalization without target annotations. The results highlight its potential as a structure-aware and domainrobust framework for large-scale EM segmentation. Our code is available at https://github.com/RNZhou0529/DiSMemNet.
This paper introduces GLUS, a novel training-free plug-and-play framework for ultrasound (US) super-resolution. GLUS extends the residual plug-and-play paradigm by incorporating a tailored guidance energy function to capture the domain-specific characteristics of US data. The proposed framework operates without the need for pre-training, offering a practical and efficient solution for enhancing US image resolution. Experiments on both simulated and in vivo data show that GLUS consistently outperforms existing benchmarks, achieving superior quantitative and qualitative results.
Personalized Federated Learning (PFL) presents a promising paradigm for mitigating privacy concerns and addressing data heterogeneity in medical images. However, the presence of data heterogeneity can introduce bias into the global model. As a result, the biased global model may degrade local performance in the personalization phase. To tackle this challenge, we propose FedLP, a novel framework that enhances global aggregation fairness and improves personalized performance. First, we propose a layer-wise aggregation strategy on the server, where it dynamically estimates each client's contribution based on the gradient discrepancies. Consider the aggregated global model may not consistently align with local distributions, we then introduce an adaptive parameter personalization process on the client side. By selectively instituting local parameters with the global model parameter with maximizing consensus optimization, clients can better retain global knowledge they need and maintain important model parameters to local data distributions. Experimental results demonstrate that FedLP outperforms state-of-the-art methods and achieves fairer performance for several medical image classification tasks.
