The human brain, as the most complex organ, comprises billions of neurons forming intricate and dynamic networks. This complexity results in brain disorders exhibiting multifaceted manifestations, both in their pathological mechanisms and clinical symptoms, thereby posing significant challenges for accurate diagnosis and effective treatment. In the light of this, the development of advanced diagnostic techniques and analytical methodologies has become increasingly crucial. While graph learning has promising performance in modeling neuroimaging data, its reliance on pairwise (binary) relationships limits its capacity to capture higher-order interactions among brain regions. Hypergraph learning frameworks address this shortcoming by modeling complex, multi-way relationships, offering a richer and more expressive mathematical foundation for understanding brain network structure and function. This survey provides a comprehensive overview of recent advancements in hypergraph learning for neuroimaging-based brain disorder diagnosis, discussing current methodological challenges and outlining promising directions for future research in this rapidly evolving field.

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.

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.