Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R²) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R² of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO₂), silicon dioxide (SiO₂) and silver (Ag), and we achieved a prediction performance with an R² value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.

Deep learning (DL) models in breast ultrasound (BUS) image analysis face challenges with data imbalance and limited atypical tumor samples. Generative Adversarial Networks (GAN) address these challenges by providing efficient data augmentation for small datasets. However, current GAN approaches fail to capture the structural features of BUS and generated images lack structural legitimacy and are unrealistic. Furthermore, generated images require manual annotation for different downstream tasks before they can be used. Therefore, we propose a two-stage GAN framework, 2s-BUSGAN, for generating annotated BUS images. It consists of the Mask Generation Stage (MGS) and the Image Generation Stage (IGS), generating benign and malignant BUS images using corresponding tumor contours. Moreover, we employ a Feature-Matching Loss (FML) to enhance the quality of generated images and utilize a Differential Augmentation Module (DAM) to improve GAN performance on small datasets. We conduct experiments on two datasets, BUSI and Collected. Moreover, results indicate that the quality of generated images is improved compared with traditional GAN methods. Additionally, our generated images underwent evaluation by ultrasound experts, demonstrating the possibility of deceiving doctors. A comparative evaluation showed that our method also outperforms traditional GAN methods when applied to training segmentation and classification models. Our method achieved a classification accuracy of 69% and 85.7% on two datasets, respectively, which is about 3% and 2% higher than that of the traditional augmentation model. The segmentation model trained using the 2s-BUSGAN augmented datasets achieved DICE scores of 75% and 73% on the two datasets, respectively, which were higher than the traditional augmentation methods. Our research tackles imbalanced and limited BUS image data challenges. Our 2s-BUSGAN augmentation method holds potential for enhancing deep learning model performance in the field.

Due to the heterogeneity of ultrasound (US) images and the indeterminate US texture of liver fibrosis (LF), automatic evaluation of LF based on US images is still challenging. Thus, this study aimed to propose a hierarchical Siamese network that combines the information from liver and spleen US images to improve the accuracy of LF grading. There were two stages in the proposed method. In stage one, a dual-channel Siamese network was trained to extract features from paired liver and spleen patches that were cropped from US images to avoid vascular interferences. Subsequently, the L1 distance was used to quantify the liver–spleen differences (LSDs). In stage two, the pretrained weights from stage one were transferred into the Siamese feature extractor of the LF staging model, and a classifier was trained using the fusion of the liver and LSD features for LF staging. This study was retrospectively conducted on US images of 286 patients with histologically proven liver fibrosis stages. Our method achieved a precision and sensitivity of 93.92% and 91.65%, respectively, for cirrhosis (S4) diagnosis, which is about 8% higher than that of the baseline model. The accuracy of the advanced fibrosis (≥S3) diagnosis and the multi-staging of fibrosis (≤S2 vs. S3 vs. S4) both improved about 5% to reach 90.40% and 83.93%, respectively. This study proposed a novel method that combined hepatic and splenic US images and improved the accuracy of LF staging, which indicates the great potential of liver–spleen texture comparison in noninvasive assessment of LF based on US images.

Circle detection remains a critical yet challenging task in computer vision, particularly under complex imaging conditions where existing measurement methods face persistent challenges in parameter configuration and noise resilience. This paper presents a novel circle detection algorithm based on two perceptually grounded parameters: the perceptual length difference resolution λ, derived from human cognitive models, and the minimum distinguishable distance threshold K, determined through empirical observations. The algorithm implements a local stochastic sampling strategy integrated with a bottom-up circular search mechanism, with all critical parameters in the algorithm derived adaptively based on λ and K, eliminating the need for repetitive hyperparameter search processes. Experiments demonstrate that our methodology achieves an exceptional Fscore of 85.5% on the public circle detection dataset, surpassing state-of-the-art approaches by approximately 7.3%. Notably, the framework maintains robust detection capability (Fscore = 85%) under extreme noise conditions (50% Gaussian noise contamination), maintaining superior performance relative to comparative methods. The adaptive parameterization strategy provides insights for developing vision systems that bridge computational efficiency with human perceptual robustness.

Fundus diseases are among the leading causes of visual impairment and blindness, many of which could be prevented through early intervention. Ultra-widefield fundus (UWF) imaging, offering a wide field of view and enabling non-mydriatic acquisition, has emerged as an ideal modality for screening; however, the volume of cases far exceeds the diagnostic capacity of ophthalmologists, necessitating an automated diagnostic system. Nevertheless, existing deep learning algorithms often encounter challenges including high computational costs, large data requirements, and performance degradation on out-of-distribution data. Domain-adaptive fine-tuning of existing multimodal large models offers a promising pathway to address these limitations. Here, we developed an anomaly detection framework for UWF images based on CLIP. First, we proposed a dual-pathway detection mechanism for accurate anomaly detection. Second, a lightweight hybrid low-rank adapter is designed for parameter-efficient fine-tuning to bridge the domain gap between natural images and fundus images. Moreover, to address the scarcity and variability of abnormal samples, we developed an anomaly synthesis algorithm based on Poisson-blending to broaden and generalize the anomaly paradigm. With only 32 pairs of training samples, the framework achieved efficient and robust anomaly detection. The framework was evaluated on 8 independent cross-source datasets (29,739 images covering >50 anomaly patterns), yielding an average AUC of 85.93% and a peak AUC of 94.54%. Owing to its low computational and data requirements, this framework provides a practical and scalable solution for fundus disease screening and shows strong potential for clinical translation. Code and details are available through https://github.com/JohnLeo-XJTU/AnomalyUWF.