Deep learning offers notable promise for computational pathology, but its performance is constrained by the need for extensively annotated datasets, which are costly and laborious to produce. Self-Supervised Learning (SSL) provides an effective paradigm for learning discriminative representations from unannotated pathological images. However, existing SSL methods often overlook domain-specific characteristics of pathological images and suffer from the adverse effects of low-quality negative samples, leading to sub-optimal feature representations for downstream tasks. To overcome these limitations, we propose a novel Domain-Specific Self-supervised Contrastive Learning (DSSCL) framework, which incorporates two novel components: (1) a Stain-Separation Based Data Augmentation (SSDA) module that enhances stain-aware representation learning by fusing stain-separated components with original hematoxylin and eosin images, and (2) a Contrast-Aware Pair Refinement (CAPR) module that improves feature discriminability by filtering potential positives and mining hard negatives, thereby mitigating the influence of low-quality negatives. Extensive experiments demonstrate that DSSCL achieves comparable accuracy in classification tasks using only 0.1% labeled data compared to a network fine-tuned from ImageNet with 10% labeled data, while also delivering competitive performance in detection and segmentation tasks, underscoring its effectiveness in learning transferable and robust feature representations across diverse downstream tasks. The code is available at https://github.com/junjianli106/DSSCL.

The increase in cancer drug resistance poses an enormous challenge in implementing effective therapeutic interventions. Combination therapy has emerged as an effective method to combat this resistance, but traditional methods for identifying viable drug combinations are often cumbersome and resource intensive. Recently, computational models have been developed to simplify the prediction of viable drug combinations, thereby improving the efficiency of this field of research. However, many existing models treat drug combinations independently, ignoring the crucial interaction dynamics between them. Moreover, these models fail to exploit the complementary insights provided by cell line multiomics data. In this work, we propose MVCASyn, an innovative deep learning model that predicts synergistic drug combinations. Compared with existing models, MVCASyn combines a dual-view representation learning module to precisely extract the multilevel features of atomic interactions, and adopts a cross-attention mechanism to fuse cell line multiomics data. Our experimental results show that MVCASyn consistently outperforms the current advanced models across all the evaluation metrics. Visualization experiments of drug atomic importance scores further emphasize the ability of MVCASyn to identify key drug substructures. A case study experiment also confirms that MVCASyn is effective in practical applications. The code of MVCASyn is publicly accessible at https://doi.org/10.57760/sciencedb.31476.

Accurately predicting the survival of patients with esophageal cancer after esophagectomy is crucial for clinical precision treatment. However, the existing methods of predicting Overall Survival time (OStime) mostly build supervised learning with the uncensored data, ignoring the potential information hidden in the censored data. To utilize the information hidden in the clinically abundant censored data, we propose a Semi-Supervised Learning with Adaptive pseudo-label Selection and Correction (SSLASC) to predict the OStime of esophageal cancer using both uncensored and censored data. Specifically, we first transform the OStime regression problem to a classification task followed by Softmax Expected Value Refinement (SEVR) and train a Transformer network using the uncensored data, which is then used to predict the OStime for the censored data. Secondly, we design an adaptive pseudo-label selection strategy to dynamically select more classes and more balanced samples from the predicted censored data by allocating adaptive thresholds for different classes of samples when performing pseudo-label selection. Finally, a distribution correction and a meta label correction modules are proposed to make the selected pseudo-labels closer to the real overall OStime. We test SSLASC on an internal dataset and two external datasets with sample sizes of 327, 104, and 16, respectively. The experimental results demonstrate that SSLASC achieves Mean Absolute Error (MAE) of 12.23, 12.64, and 12.47 months on the three test datasets. Compared to the optimal State-Of-The-Art (SOTA) method, SSLASC improves performance by 1.09, 1.07, and 1.09 months, respectively. In addition, SSLASC also achieves the best performance in dichotomized survival analysis.

MicroRNAs (miRNAs) play a key role in the prevention, diagnosis, and treatment of complex diseases. However, identifying miRNA-disease associations (MDAs) through traditional methods is costly and time-consuming. Recent studies have reported numerous validated MDAs, forming the basis for the prediction of new MDAs using computational methods. In this study, we propose SAETNMDA, a computational method that applies fast kernel learning (FKL) and variant triplet networks to predict MDAs. First, miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data. Next, feature representations are obtained by applying stacked autoencoders (SAEs) and triplet networks, enabling the identification of associated pairs by mapping them to nearby locations in the embedding space, while unassociated ones are mapped distantly. Finally, we utilize XGBoost (Extreme Gradient Boosting) to obtain predictive scores for MDAs from these features. SAETNMDA’s performance is evaluated with 5-fold cross-validation (5-fold-CV) and compared with other methods. It achieves the highest AUC and AUPR (0.9419, 0.4749 for HMDD v2.0; 0.9496, 0.5355 for HMDD v3.2, respectively). The performance is also validated on an independent dataset and de novo miRNAs, with SAETNMDA achieving the highest AUC and AUPR in all validations. Case studies also demonstrate the robust predictive capability of our method, with the top 50 predicted miRNAs validated for each of the three diseases. These results highlight SAETNMDA as an efficient model for MDA prediction. SAETNMDA’s source code is available at https://github.com/npxquynhdhsp/SAETNMDA.

A novel hybrid model combining a convolutional neural network (CNN) and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography scans. This approach is crucial as it assists clinicians in identifying patients likely to benefit from treatment and in assessing their prognosis. The model employs channel splitting to minimize parameter count. It then leverages both CNN for local feature extraction and a streamlined Transformer for global feature comprehension. To enhance efficiency, a novel self-attention mechanism is implemented, focusing on feature aggregation and element-wise multiplication. To address the different semantic meanings of features, an attention-based module is designed to seamlessly integrate features from both networks, employing a process of coarse fusion, attention computation, and fine fusion. When evaluated with data from 232 lung cancer patients who have undergone neoadjuvant chemoimmunotherapy, the model demonstrates exceptional performance, achieving a Dice score of 47.04% and a 95.00% Hausdorff distance of 25.12 mm, outperforming existing methods. Additionally, it has only 2.91×10⁶ parameters and 52.95×10⁹ floating point operations. Moreover, the model’s predictive accuracy in tumor diameter estimation is beneficial for treatment planning. Its robustness is further validated through its application in stroke lesion prediction, indicating its broad applicability.

Predicting mortality risk in the Intensive Care Unit (ICU) using Electronic Medical Records (EMR) is crucial for identifying patients in need of immediate attention. However, the incompleteness and the variability of EMR features for each patient make mortality prediction challenging. This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges. The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data, enabling the effective and explainable fusion of the incomplete features. Modality-specific encoders are employed to encode each modality feature separately. To tackle the variability and incompleteness of input features among patients, a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs. Furthermore, a MultiModal Gated Contrastive Representation Learning (MMGCRL) method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance. We evaluate the proposed framework using the large-scale ICU dataset, MIMIC-III. Experimental results demonstrate its effectiveness in mortality prediction, outperforming several state-of-the-art methods.

Although multi-view monitoring techniques have been widely applied in skinned model reconstruction and movement analysis, traditional systems using high-performance Personal Computers (PCs), or industrial cameras are often prohibitive due to high costs and limited scalability. Here, we introduce an affordable, scalable multi-view image acquisition system for skinned model reconstruction in animal studies, utilizing consumer Android devices and a wireless network for synchronized monitoring named Rodent Arena Multi-View Monitor (RAMM). It uses smartphones as camera nodes with local data storage, enabling cost-effective scalability. Its custom synchronization solution and portability make it ideal for research and education in rodent behavior analysis, offering a practical alternative for institutions with limited budgets. Furthermore, the portability and flexibility of this system make it an ideal tool for rodent skinned model research based on multi-view image acquisition. To evaluate the performance, we perform an oscilloscope analysis to ensure effectiveness of synchronization. A 45-camera node setup is built to highlight RAMM’s cost efficiency and ease in constructing large-scale systems. Additionally, the data quality is validated using the Instant Neural Graphics Primitives (Instant-NGP) method. Remarkable results were achieved with a 30.49 dB PSNR by utilizing only 25 images with intrinsic and extrinsic parameters, fulfilling the requirements for well-synchronized data used in 3D representation algorithms.

Glioma survival risk prediction is of great significance for the individualized treatment and assessment programs. Currently, most deep learning based survival prediction paradigms rely on invasive and expensive histopathology and genomics methods. However, magnetic resonance imaging (MRI) has emerged as a promising non-invasive alternative with significant prognostic potential. To leverage the benefits of MRI, we propose a segmentation-guided fully automated multimodal MRI-based survival network (SGS-Net), which can simultaneously perform glioma segmentation and survival risk prediction. Specifically, the task interrelation is addressed using a hybrid convolutional neural network-Transformer (CNN-Transformer) encoder to represent the shared high-level semantic features by co-training a decoder for glioma segmentation and a Cox model for survival prediction. Then, to ensure the effective representation of the high-level features, glioma segmentation as an auxiliary task is utilized to guide survival prediction by jointly optimizing the segmentation loss and the Cox partial log-likelihood loss. Furthermore, a pair-wise ranking loss is designed to allow the network to learn the survival difference between patients. To balance the multi-task losses, an uncertain weight manner is adopted to adaptively adjust the weights for preventing task bias. Finally, the proposed SGS-Net is assessed using a publicly available multi-institutional dataset. Experimental and visual results show that SGS-Net achieves promising segmentation performance and obtains a C-index of 81.07% for survival risk prediction, which outperforms several existing state-of-the-art methods and even histopathology-based methods. In addition, Kaplan-Meier survival analysis confirms that the prognosis risk generated by SGS-Net is consistent with the prior prognosis based on the grading or genotyping paradigms.

Automated diagnosis of chest X-rays is pivotal in radiology, aiming to alleviate the workload of radiologists. Traditional methods primarily rely on visual features or label dependence, which is a limitation in detecting nuanced or rare lesions. To address this, we present KEXNet, a pioneering knowledge-enhanced X-ray lesion detection model. KEXNet employs a unique strategy akin to expert radiologists, integrating a knowledge graph based on expert annotations with an interpretable graph learning approach. This novel method combines object detection with a graph neural network, facilitating precise local lesion detection. For global lesion detection, KEXNet synergizes knowledge-enhanced local features with global image features, enhancing diagnostic accuracy. Our evaluations on three benchmark datasets demonstrate that KEXNet outshines existing models, particularly in identifying small or infrequent lesions. Notably, on the Chest ImaGenome dataset, KEXNet’s AUC for local lesion detection surpasses 8.9% compared to the state-of-the-art method AnaXNet, showcasing its potential in revolutionizing automated chest X-ray diagnostics.

Event Extraction (EE) is a key task in information extraction, which requires high-quality annotated data that are often costly to obtain. Traditional classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained annotations. While recent approaches have endeavored to address EE through a more data-efficient generative process, they often overlook event keywords, which are vital for EE. To tackle these challenges, we introduce KeyEE, a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE). We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language model. With the auxiliary sub-prompt, KeyEE learns event keywords knowledge implicitly, thereby reducing the dependence on annotated data. Furthermore, we investigate and analyze various EKE sub-prompt strategies to encourage further research in this area. Our experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.