Accurate medical image segmentation is essential for reliable diagnosis, surgical planning, and disease monitoring. Semi-supervised medical image segmentation offers great potential by exploiting abundant unlabeled data with limited annotations, but it is prone to confirmation bias. To overcome this, we propose Attention Inverted Feature Perturbation (AIFP), a novel method that adaptively inverts feature-level attention weights to generate perturbations. This strategy encourages diversity and maintains independence between networks within a co-training framework, thereby mitigating confirmation bias. Extensive experiments on four public benchmarks validate the effectiveness of AIFP. Specifically, our method achieves Dice scores of 89.90% on ACDC and 91.01% on LA using only 10% labeled data, and 84.58% on Pancreas-NIH and 82.58% on PROMISE12 using 20% labeled data. These results consistently outperform state-of-the-art semi-supervised approaches, highlighting the practical value of AIFP in advancing accurate and robust medical image segmentation. AIFP enables reliable segmentation with limited annotations, supporting critical tasks such as left atrium delineation, pancreas boundary identification, and prostate segmentation. By reducing annotation demands while ensuring robustness, it has the potential to accelerate the clinical adoption of artificial intelligence-driven imaging tools.

Multimodal pathological images are usually in clinical diagnosis, but computer vision-based multimodal image-assisted diagnosis faces challenges with modality fusion, especially in the absence of expert-annotated data. To achieve the modality fusion in multimodal images with label scarcity, we propose a novel ``pretraining + fine-tuning" framework for multimodal semi-supervised medical image classification. Specifically, we propose a synergistic learning pretraining framework of consistency, reconstructive, and aligned learning. By treating one modality as an augmented sample of another modality, we implement a self-supervised learning pre-train, enhancing the baseline model's feature representation capability. Then, we design a fine-tuning method for multimodal fusion. During the fine-tuning stage, we set different encoders to extract features from the original modalities and provide a multimodal fusion encoder for fusion modality. In addition, we propose a distribution shift method for multimodal fusion features, which alleviates the prediction uncertainty and overfitting risks caused by the lack of labeled samples. We conduct extensive experiments on the publicly available gastroscopy image datasets Kvasir and Kvasirv2. Quantitative and qualitative results demonstrate that the proposed method outperforms the current state-of-the-art classification methods. The code will be released at: https://github.com/LQH89757/MICS.

The clinical significance and molecular mechanisms of KLHDC4 in malignancies, particularly in clear cell renal cell carcinoma (ccRCC), remain poorly understood. In this study, we integrated multi-omics data—including transcriptomic, proteomic, and single-cell RNA sequencing—to systematically evaluate the clinical relevance and functional role of KLHDC4, followed by experimental validation. Our results demonstrated that KLHDC4 is significantly upregulated in multiple cancer types and serves as an independent prognostic biomarker for poor survival in ccRCC. KLHDC4 expression was correlated with immune cell infiltration, tumor mutational profiles, and immune checkpoint expression. Furthermore, KLHDC4 levels predicted response to drug therapies, including immunotherapy and tyrosine kinase inhibitors. Functional experiments showed that KLHDC4 knockdown suppressed proliferation, migration, and invasion in vitro, as well as tumor growth in vivo, whereas its overexpression promoted malignant phenotypes. Mechanistic investigations through RNA sequencing and Western blot analysis indicated that KLHDC4 activates the PI3K/AKT signaling pathway. Rescue assays confirmed that KLHDC4-driven oncogenic effects are partially mediated through this axis. Additionally, molecular docking identified ledipasvir as a potential KLHDC4 inhibitor. Collectively, our findings establish KLHDC4 as a novel prognostic biomarker and therapeutic predictor, highlighting the KLHDC4/PI3K/AKT axis as a potential target for ccRCC treatment.