Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence

Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report generation, yet inference-time hallucinations still undermine trustworthy use: models can produce fluent conclusions that conflict with imaging evidence. Existing mitigation strategies typically rely on additional training, external retrieval/knowledge bases, or multi-stage post-hoc verification, which increases cost and pipeline complexity and often generalizes poorly across models and tasks.To address this, we propose a holistic, training-free evidence-injection framework that systematically mitigates hallucinations through dual-side evidence injection. By leveraging ROI priors acquired using MedSAM in our implementation, we recalibrate the visual perception trajectory via ROI-guided activation modulation while anchoring the textual reasoning trajectory by mapping anatomical coordinates into discrete semantic tokens as verifiable external memory. Then we introduce a task-aware dynamic router to select modality-specific interventions based on task semantics, balancing perceptual grounding and linguistic fluency. We conduct systematic evaluations on 2 tasks and 5 datasets using \texttt{LLaVA-1.5-7B}, \texttt{LLaVA-Med-1.5-7B}, \texttt{Qwen3-VL-8B/32B}, and \texttt{InternVL-3.5-8B/38B}. Controlled ablations and visualizations further validate the framework, which consistently outperforms baselines across medical benchmarks, improving close-ended accuracy by up to $\sim\mathbf{6}\%\uparrow$ and reducing open-ended hallucinations by $\sim\mathbf{35}\%\downarrow$. The code has been made available on GitHub: \href{https://github.com/Henry991115/SPRG}{\textcolor{blue}{https://github.com/Henry991115/SPRG}}.

Multi-scale Cascaded Large-Model for Whole-body ROI Segmentation

Organs-at-risk segmentation is critical for ensuring the safety and precision of radiotherapy and surgical procedures. However, existing methods for organs-at-risk image segmentation often suffer from uncertainties and biases in target selection, as well as insufficient model validation experiments, limiting their generality and reliability in practical applications. To address these issues, we propose an innovative cascaded network architecture called the Multi-scale Cascaded Fusing Network (MCFNet), which effectively captures complex multi-scale and multi-resolution features. MCFNet includes a Sharp Extraction Backbone and a Flexible Connection Backbone, which respectively enhance feature extraction in the downsampling and skip-connection stages. This design not only improves segmentation accuracy but also ensures computational efficiency, enabling precise detail capture even in low-resolution images. We conduct experiments using the A6000 GPU on diverse datasets from 671 patients, including 36,131 image-mask pairs across 10 different datasets. MCFNet demonstrates strong robustness, performing consistently well across 10 datasets. Additionally, MCFNet exhibits excellent generalizability, maintaining high accuracy in different clinical scenarios. We also introduce an adaptive loss aggregation strategy to further optimize the model training process, improving both segmentation accuracy and efficiency. Through extensive validation, MCFNet demonstrates superior performance compared to existing methods, providing more reliable image-guided support. Our solution aims to significantly improve the precision and safety of radiotherapy and surgical procedures, advancing personalized treatment. The code has been made available on GitHub:https://github.com/Henry991115/MCFNet.

Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the re-sampled data from estimated bottleneck space have consistent clustering properties to progressively improve the deep clustering model. Comprehensive experiments on a variety of datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality when compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.