Superhydrophobicity-enabled jumping-droplet condensation and frosting have great potential in various engineering applications, ranging from heat transfer processes to antifog/frost techniques. However, monitoring such droplets is challenging due to the high frequency of droplet behaviors, cross-scale distribution of droplet sizes, and diversity of surface morphologies. Leveraging deep learning, we develop a semisupervised framework that monitors the optical observable process of condensation and frosting. This system is adept at identifying transient droplet distributions and dynamic activities, such as droplet coalescence, jumping, and frosting, on a variety of superhydrophobic surfaces. Utilizing this transient and dynamic information, various physical properties, such as heat flux, jumping characteristics, and frosting rate, can be further quantified, conveying the heat transfer and antifrost performances of each surface perceptually and comprehensively. Furthermore, this framework relies on only a small amount of annotated data and can efficiently adapt to new condensation conditions with varying surface morphologies and illumination techniques. This adaptability is beneficial for optimizing surface designs to enhance condensation heat transfer and antifrosting performance.

Molecular prediction tasks normally demand a series of professional experiments to label the target molecule, which suffers from the limited labeled data problem. One of the semisupervised learning paradigms, known as self-training, utilizes both labeled and unlabeled data. Specifically, a teacher model is trained using labeled data and produces pseudo labels for unlabeled data. These labeled and pseudo-labeled data are then jointly used to train a student model. However, the pseudo labels generated from the teacher model are generally not sufficiently accurate. Thus, we propose a robust self-training strategy by exploring robust loss function to handle such noisy labels in two paradigms, that is, generic and adaptive. We have conducted experiments on three molecular biology prediction tasks with four backbone models to gradually evaluate the performance of the proposed robust self-training strategy. The results demonstrate that the proposed method enhances prediction performance across all tasks, notably within molecular regression tasks, where there has been an average enhancement of 41.5%. Furthermore, the visualization analysis confirms the superiority of our method. Our proposed robust self-training is a simple yet effective strategy that efficiently improves molecular biology prediction performance. It tackles the labeled data insufficient issue in molecular biology by taking advantage of both labeled and unlabeled data. Moreover, it can be easily embedded with any prediction task, which serves as a universal approach for the bioinformatics community.

In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to robustly adapt the model itself to some unlabeled target populations using existing labeled data in a source population. However, there is a paucity of literature on transferring performance metrics, especially receiver operating characteristic (ROC) parameters, of a trained model. In this paper, we aim to evaluate the performance of a trained binary classifier on unlabeled target population based on ROC analysis. We proposed Semisupervised Transfer lEarning of Accuracy Measures (STEAM), an efficient three-step estimation procedure that employs (1) double-index modeling to construct calibrated density ratio weights and (2) robust imputation to leverage the large amount of unlabeled data to improve estimation efficiency. We establish the consistency and asymptotic normality of the proposed estimator under the correct specification of either the density ratio model or the outcome model. We also correct for potential overfitting bias in the estimators in finite samples with cross-validation. We compare our proposed estimators to existing methods and show reductions in bias and gains in efficiency through simulations. We illustrate the practical utility of the proposed method on evaluating prediction performance of a phenotyping model for rheumatoid arthritis (RA) on a temporally evolving EHR cohort.

UNASSIGNED: Accurate segmentation of pancreatic cancer tumors using positron emission tomography/computed tomography (PET/CT) multimodal images is crucial for clinical diagnosis and prognosis evaluation. However, deep learning methods for automated medical image segmentation require a substantial amount of manually labeled data, making it time-consuming and labor-intensive. Moreover, addition or simple stitching of multimodal images leads to redundant information, failing to fully exploit the complementary information of multimodal images. Therefore, we developed a semisupervised multimodal network that leverages limited labeled samples and introduces a cross-fusion and mutual information minimization (MIM) strategy for PET/CT 3D segmentation of pancreatic tumors.
UNASSIGNED: Our approach combined a cross multimodal fusion (CMF) module with a cross-attention mechanism. The complementary multimodal features were fused to form a multifeature set to enhance the effectiveness of feature extraction while preserving specific features of each modal image. In addition, we designed an MIM module to mitigate redundant high-level modal information and compute the latent loss of PET and CT. Finally, our method employed the uncertainty-aware mean teacher semi-supervised framework to segment regions of interest from PET/CT images using a small amount of labeled data and a large amount of unlabeled data.
UNASSIGNED: We evaluated our combined MIM and CMF semisupervised segmentation network (MIM-CMFNet) on a private dataset of pancreatic cancer, yielding an average Dice coefficient of 73.14%, an average Jaccard index score of 60.56%, and an average 95% Hausdorff distance (95HD) of 6.30 mm. In addition, to verify the broad applicability of our method, we used a public dataset of head and neck cancer, yielding an average Dice coefficient of 68.71%, an average Jaccard index score of 57.72%, and an average 95HD of 7.88 mm.
UNASSIGNED: The experimental results demonstrate the superiority of our MIM-CMFNet over existing semisupervised techniques. Our approach can achieve a performance similar to that of fully supervised segmentation methods while significantly reducing the data annotation cost by 80%, suggesting it is highly practicable for clinical application.

A novel approach for video instance segmentation is presented using semisupervised learning. Our Cluster2Former model leverages scribble-based annotations for training, significantly reducing the need for comprehensive pixel-level masks. We augment a video instance segmenter, for example, the Mask2Former architecture, with similarity-based constraint loss to handle partial annotations efficiently. We demonstrate that despite using lightweight annotations (using only 0.5% of the annotated pixels), Cluster2Former achieves competitive performance on standard benchmarks. The approach offers a cost-effective and computationally efficient solution for video instance segmentation, especially in scenarios with limited annotation resources.

UNASSIGNED: To use a diffusion-based deep learning model to recover bone microstructure from low-resolution images of the proximal femur, a common site of traumatic osteoporotic fractures.
UNASSIGNED: Training and testing data in this retrospective study consisted of high-resolution cadaveric micro-CT scans (n = 26), which served as ground truth. The images were downsampled prior to use for model training. The model was used to increase spatial resolution in these low-resolution images threefold, from 0.72 mm to 0.24 mm, sufficient to visualize bone microstructure. Model performance was validated using microstructural metrics and finite element simulation-derived stiffness of trabecular regions. Performance was also evaluated across a handful of image quality assessment metrics. Correlations between model performance and ground truth were assessed using intraclass correlation coefficients (ICCs) and Pearson correlation coefficients.
UNASSIGNED: Compared with popular deep learning baselines, the proposed model exhibited greater accuracy (mean ICC of proposed model, 0.92 vs ICC of next best method, 0.83) and lower bias (mean difference in means, 3.80% vs 10.00%, respectively) across the physiologic metrics. Two gradient-based image quality metrics strongly correlated with accuracy across structural and mechanical criteria (r > 0.89).
UNASSIGNED: The proposed method may enable accurate measurements of bone structure and strength with a radiation dose on par with current clinical imaging protocols, improving the viability of clinical CT for assessing bone health.Keywords: CT, Image Postprocessing, Skeletal-Appendicular, Long Bones, Radiation Effects, Quantification, Prognosis, Semisupervised Learning Online supplemental material is available for this article. © RSNA, 2023.

BACKGROUND: Automatic skin lesion recognition has shown to be effective in increasing access to reliable dermatology evaluation; however, most existing algorithms rely solely on images. Many diagnostic rules, including the 3-point checklist, are not considered by artificial intelligence algorithms, which comprise human knowledge and reflect the diagnosis process of human experts.
OBJECTIVE: In this paper, we aimed to develop a semisupervised model that can not only integrate the dermoscopic features and scoring rule from the 3-point checklist but also automate the feature-annotation process.
METHODS: We first trained the semisupervised model on a small, annotated data set with disease and dermoscopic feature labels and tried to improve the classification accuracy by integrating the 3-point checklist using ranking loss function. We then used a large, unlabeled data set with only disease label to learn from the trained algorithm to automatically classify skin lesions and features.
RESULTS: After adding the 3-point checklist to our model, its performance for melanoma classification improved from a mean of 0.8867 (SD 0.0191) to 0.8943 (SD 0.0115) under 5-fold cross-validation. The trained semisupervised model can automatically detect 3 dermoscopic features from the 3-point checklist, with best performances of 0.80 (area under the curve [AUC] 0.8380), 0.89 (AUC 0.9036), and 0.76 (AUC 0.8444), in some cases outperforming human annotators.
CONCLUSIONS: Our proposed semisupervised learning framework can help with the automatic diagnosis of skin disease based on its ability to detect dermoscopic features and automate the label-annotation process. The framework can also help combine semantic knowledge with a computer algorithm to arrive at a more accurate and more interpretable diagnostic result, which can be applied to broader use cases.

A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training. This paper proposes a novel semisupervised hyperspectral deep learning model based on a generative adversarial network, utilizing an improved 1D U-Net as its discriminator, to detect FMs on raw chicken breast fillets. The model was trained by using approximately 879,000 spectral responses from hyperspectral images of clean chicken breast fillets in the near-infrared wavelength range of 1000-1700 nm. Testing involved 30 different types of FMs commonly found in processing plants, prepared in two nominal sizes: 2 × 2 mm2 and 5 × 5 mm2. The FM-detection technique achieved impressive results at both the spectral pixel level and the foreign material object level. At the spectral pixel level, the model achieved a precision of 100%, a recall of over 93%, an F1 score of 96.8%, and a balanced accuracy of 96.9%. When combining the rich 1D spectral data with 2D spatial information, the FM-detection accuracy at the object level reached 96.5%. In summary, the impressive results obtained through this study demonstrate its effectiveness at accurately identifying and localizing FMs. Furthermore, the technique\'s potential for generalization and application to other agriculture and food-related domains highlights its broader significance.

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning. Instead of labeling individual instances, humans provide supervision to data regions, which are subspaces of the input data space, representing subpopulations of data. Since labeling now is performed on a region level, 0/1 labeling becomes imprecise. Thus, we design the region label to be a qualitative assessment of the class proportion, which coarsely preserves the labeling precision but is also easy for humans to do. To identify informative regions for labeling and learning, we further devise a hierarchical active learning process that recursively constructs a region hierarchy. This process is semisupervised in the sense that it is driven by both active learning strategies and human expertise, where humans can provide discriminative features. To evaluate our framework, we conducted extensive experiments on nine datasets as well as a real user study on a survival analysis of colorectal cancer patients. The results have clearly demonstrated the superiority of our region-based active learning framework against many instance-based active learning methods.

BACKGROUND: Observational biomedical studies facilitate a new strategy for large-scale electronic health record (EHR) utilization to support precision medicine. However, data label inaccessibility is an increasingly important issue in clinical prediction, despite the use of synthetic and semisupervised learning from data. Little research has aimed to uncover the underlying graphical structure of EHRs.
OBJECTIVE: A network-based generative adversarial semisupervised method is proposed. The objective is to train clinical prediction models on label-deficient EHRs to achieve comparable learning performance to supervised methods.
METHODS: Three public data sets and one colorectal cancer data set gathered from the Second Affiliated Hospital of Zhejiang University were selected as benchmarks. The proposed models were trained on 5% to 25% labeled data and evaluated on classification metrics against conventional semisupervised and supervised methods. The data quality, model security, and memory scalability were also evaluated.
RESULTS: The proposed method for semisupervised classification outperforms related semisupervised methods under the same setup, with the average area under the receiver operating characteristics curve (AUC) reaching 0.945, 0.673, 0.611, and 0.588 for the four data sets, respectively, followed by graph-based semisupervised learning (0.450, 0.454, 0.425, and 0.5676, respectively) and label propagation (0.475,0.344, 0.440, and 0.477, respectively). The average classification AUCs with 10% labeled data were 0.929, 0.719, 0.652, and 0.650, respectively, comparable to that of the supervised learning methods logistic regression (0.601, 0.670, 0.731, and 0.710, respectively), support vector machines (0.733, 0.720, 0.720, and 0.721, respectively), and random forests (0.982, 0.750, 0.758, and 0.740, respectively). The concerns regarding the secondary use of data and data security are alleviated by realistic data synthesis and robust privacy preservation.
CONCLUSIONS: Training clinical prediction models on label-deficient EHRs is indispensable in data-driven research. The proposed method has great potential to exploit the intrinsic structure of EHRs and achieve comparable learning performance to supervised methods.