Emotion recognition using functional near-infrared spectroscopy (fNIRS) shows increasing potential for decoding neurophysiological responses to emotional stimuli. However, existing methods suffer from local noise sensitivity, limited ability to capture global spatiotemporal patterns, and high dynamic complexity at high temporal and frequency scales. To address these challenges, we propose DTFMformer, a dual-bone dynamic time-frequency masked transformer-based framework for robust three-class emotion recognition. The temporal bone incorporates a dynamic temporal masking strategy to suppress local noise interference and temporally unstable fluctuations, and employs a transformer-based temporal auto-en/decoder to extract hierarchical temporal features with global contextual awareness. The frequency bone employs a dynamic frequency interpolation and masking strategy, which adaptively masks lowsalience spectral components based on inter-sample amplitude differences, and utilizes a transformer-based frequency autodecoder to capture long-range dependencies in the spectral domain. Subsequently, the features from both bones are fused via a multi-head cross-attention to enable deep integration and joint modeling of time-frequency information. Experimental results on the public NEMO dataset and a self-collected validation dataset demonstrate that DTFMformer consistently outperforms existing state-of-the-art methods, achieving peak classification accuracies of 86.9% and 87.5%, respectively. These findings highlight the effectiveness and interpretability of DTFMformer in advancing fNIRS-based emotion state decoding.

Hepatocellular carcinoma is a highly heterogeneous and complex malignant tumor, posing significant challenges for precise diagnosis and treatment. Existing methods face limitations in multimodal data integration and multi-phase feature modeling, making it difficult to fully exploit the complementary information from multi-phase CT images, structured clinical data, and medical texts. On one hand, most methods rely solely on single-modal data (e.g. CT images or clinical reports), failing to effectively utilize the complementary characteristics of multimodal data. On the other hand, traditional approaches typically concatenate multi-phase CT images as input to the model, ignoring the dynamic evolution features between different phases, leading to information loss and limited predictive performance. To address these issues, we propose a novel Tri-modal Phase-aware Contrastive Learning Framework (TPCL), which incorporates a Phase-aware Attention Fusion Network (PAAF-Net) and a Phase-Conditional Prompt Network (PCPN) to achieve deep alignment and integration of multimodal features. Additionally, we design a Multi-modal Contrastive Loss to further optimize the consistency of feature distributions across different modalities. Experimental results on multiple public and private datasets demonstrate that TPCL significantly outperforms existing methods, achieving up to an$18.54\%$improvement in ACC and a$10.09\%$improvement in AUC.

Subtype classification of medical images is a key challenge in medical diagnosis. However, due to scarce labeled data, data quality issues, and poor generalization ability of existing models, certain limitations exist. This study proposes a dynamic prototype multi-model fusion (DPF) framework based on a feature screening mechanism. The framework integrates ResNet and Vision Transformer architectures to extract complementary feature representations and constructs robust prototypes through distance-based threshold setting and voting mechanisms. A feature screening layer distinguishes high-quality from lowquality unlabeled samples using similarity and occurrence maps, while a collaborative training strategy utilizes both sample types to optimize prototype representations. The dynamic prototype expansion mechanism enables real-time adaptation to data distribution changes through self-training. Comprehensive experiments are conducted on two medical imaging datasets, including one public breast MRI dataset and one private CT liver dataset. The proposed framework achieves superior performance with accuracy improvements over supervised baselines and existing semi-supervised methods. The results verify the framework's effectiveness in addressing medical image heterogeneity, sample quality variations, and limited labeled data availability.

Multi-parametric magnetic resonance imaging (mpMRI) is widely used in the diagnosis of orbital lymphoproliferative disorders (OLPDs) due to its non-invasive nature. However, in clinical practice, contrast-enhanced T1-weighted (T1C) images are often unavailable due to contraindications to gadolinium-based contrast agents, meanwhile T2-weighted (T2w) images may also be omitted for time-sensitive diagnoses, making it a challenge to generate these images from T1-weighted (T1 w) image alone for multimodal differential diagnosis. Generative adversarial network (GAN)-based models partially address the issue of missing modalities in medical image analysis; however, they often suffer from unstable generation of missing images and lack integration with subsequent diagnostic tasks. To this end, we propose a One-to-Many Generative Adversarial Network (OMGAN) for diagnosing OLPDs in incomplete mpMRI, consisting of a cross-modal generator and a self-representation module, enabling multimodal diagnosis using pre-contrast images alone within a single model. Specifically, we first design an image-modality fusion module that incorporates trigonometric function coding and mixup augmentation to effectively guide the generation from T1 w to T2w and T1 C within one model. Then, we construct a cross-modal generator with a semantic disambiguation block to synthesize the missing images. Meanwhile, we use a self-representation module with a classification-guided branch to effectively extract task-relevant image features. Finally, multimodal features are fused to accomplish the differential diagnosis of OLPDs in the downstream task. Experiments on internal datasets demonstrated that OMGAN outperforms state-of-the-art GAN-based models, with the area-under-the-curve and accuracy improving by 8.18-14.04% and 12.53-16.39%, respectively. Codes are available at https://github.com/3Iasticheart/OMGAN.

Bioluminescence tomography (BLT) can provide non-invasive quantitative three-dimensional tumor information which has been widely applied in pre-clinical studies. Meanwhile, in recent years, deep learning methods have significantly improved the reconstruction resolution and speed by establishing a non-linear mapping relationship between surface-measured bioluminescence and light source distribution. However, this mapping relationship only works for specific biological tissues and light transmission processes under fixed wavelengths, resulting in poor stability and generalizability. To meet the requirements of diverse practical scenarios and inspired by more effective sparse regularization and graph representation theory, we propose a novel Graph Attention Iterative Contraction Network (GAICN) to conduct a finite element mesh spatial representation study. In the GAICN framework, two learnable spatial topological transforms based on the graph attention mechanism and an iterative contraction activation function were devised to achieve non-local feature aggregation and dynamic adjustment of weights between first-order neighboring nodes in the mesh. As a deep unrolling method, GAICN naturally inherits the coherence of surface bioluminescence with the light source in Forward-Backward Splitting (FBS), thus enhancing the generalizability, stability and interpretability of the network. Both simulation and in-vivo experiments further indicated that GAICN achieved superior reconstruction performance in terms of spatial location, dual light source resolution, stability, generalizability, as well as in-vivo practicability.

Automatic infected segmentation on CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, it is a challenge to use limited information for segmentation of small infected regions. To deal with the problem, a Multi-Lung Window Fusion Network (MLWF-Net) is proposed to sufficiently utilize CT information. In the first part of MLWF-Net, the multi-lung window block is introduced to address the long-tailed distribution of CT Hounsfield Unit for more features. Then, the adaptive feature aggregation block which combines the attention mechanism with the message passing mechanism is developed to fuse the infection features adaptively from different lung windows CT. The MLWF-Net model is evaluated on a publicly available dataset and a private dataset, compared with a variety of widely-used segmentation models. The MLWF-Net holds on better performance with some benchmarks, especially for small infected areas. The proposed model is an efficient method to alleviate the long tail problem of CT Hounsfield Unit and improve the performance of small infected areas, which can be used for the grading assessment and predictive treatment of COVID-19 patients, with significant clinical value.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

Coronary artery disease (CAD), a leading cause of mortality and morbidity, manifests as atherosclerotic plaques formed by the deposition of cholesterol and lipids within coronary walls. A plenty of machine learning methods have been developed to identify different plaques or the degree of stenosis. Few studies, however, focus on plaques vulnerability, which is crucial because vulnerable plaques are at a high risk of rupture or erosion even with less severe stenosis. To this end, we propose a weakly-supervised multi-contrastive learning network named WheelNet to differentiate vulnerable plaques from stable ones in coronary CT angiography (CCTA). Specifically, we first extract cross-sectional images along the coronary centerline and took consecutive cross-sections as one image sequence. Second, we construct a WheelNet with multiple branches to perform contrastive learning between different image sequences, dependent or independent of vulnerability labels of coronary plaques. Third, we perform patient-level feature aggregation via local-to-global feature encoding given the feature embeddings of image sequences. Finally, we differentiae patients with vulnerable coronary plaques from those with stable ones using an XGBoost classifier. Extensive experiments on the CCTA dataset of 108 patients show the superiority of our WheelNet over other state of the arts, with the diagnostic area-under-the-curve (AUC) of 0.74/0.75 with/without using vulnerability labels, respectively.

Orbital lymphoproliferative disorders (OLPDs) are complex orbital mass-like lesions ranging from benign to malignant. Precise preoperative diagnosis of OLPDs holds profound importance in facilitating timely and effective patient management. Recent studies have shown that exploiting multimodal images can boost the performance in identifying different orbital lesions. However, one or several imaging modalities are sometimes missing in practical applications, which has not yet been properly addressed in existing studies. To this end, we propose a novel OLPD diagnostic method with incomplete multimodal images based on self-/cross-representation and hypergraph ensemble. Specifically, in the first stage, we develop a self-representation network to extract unimodal features and a cross-representation network to impute missing features. In the second stage, by using unimodal features as input, we construct a hypergraph for each modality to make unimodal diagnosis; while for multimodal diagnosis we conduct a multi-view grouping fusion method to reduce the semantic gap between multimodal features and fuse multiple unimodal hypergraphs as multimodal hypergraph to perform multimodal diagnosis. In the third stage, we propose an ensemble strategy that incorporates unimodal diagnosis and multimodal diagnosis to accomplish the final decision. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art approaches.

Early identification of endometrial cancer or precancerous lesions from histopathological images is crucial for precise endometrial medical care, which however is increasing hampered by the relative scarcity of pathologists. Computer-aided diagnosis (CAD) provides an automated alternative for confirming endometrial diseases with either feature-engineered machine learning or end-to-end deep learning (DL). In particular, advanced self-supervised learning alleviates the dependence of supervised learning on large-scale human-annotated data and can be used to pre-train DL models for specific classification tasks. Thereby, we develop a novel self-supervised triplet contrastive learning (SSTCL) model for classifying endometrial histopathological images. Specifically, this model consists of one online branch and two target branches. The second target branch includes a simple yet powerful augmentation module named random mosaic masking (RMM), which functions as an effective regularization by mapping the features of masked images close to those of intact ones. Moreover, we add a bottleneck Transformer (BoT) model into each branch as a self-attention module to learn the global information by considering both content information and relative distances between features at different locations. On public endometrial dataset, our model achieved four-class classification accuracies of 77.31 ± 0.84, 80.87 ± 0.48 and 83.22 ± 0.87% using 20, 50 and 100% labeled images, respectively. When transferred to the in-house dataset, our model obtained a three-class diagnostic accuracy of 96.81% with 95% confidence interval of 95.61-98.02%. On both datasets, our model outperformed state-of-the-art supervised and self-supervised methods. Our model may help pathologists to automatically diagnose endometrial diseases with high accuracy and efficiency using limited human-annotated histopathological images.

Cerenkov luminescence tomography (CLT) provides a novel technique for 3-D noninvasive detection of radiopharmaceuticals in living subjects. However, because of the severe scattering of Cerenkov light, the reconstruction accuracy and stability of CLT is still unsatisfied. In this paper, a modified weight multispectral CLT (wmCLT) reconstruction strategy was developed which split the Cerenkov radiation spectrum into several sub-spectral bands and weighted the sub-spectral results to obtain the final result. To better evaluate the property of the wmCLT reconstruction strategy in terms of accuracy, stability and practicability, several numerical simulation experiments and in vivo experiments were conducted and the results obtained were compared with the traditional multispectral CLT (mCLT) and hybrid-spectral CLT (hCLT) reconstruction strategies. The numerical simulation results indicated that wmCLT strategy significantly improved the accuracy of Cerenkov source localization and intensity quantitation and exhibited good stability in suppressing noise in numerical simulation experiments. And the comparison of the results achieved from different in vivo experiments further indicated significant improvement of the wmCLT strategy in terms of the shape recovery of the bladder and the spatial resolution of imaging xenograft tumors. Overall the strategy reported here will facilitate the development of nuclear and optical molecular tomography in theoretical study.