Multimodal magnetic resonance imaging (MRI) is instrumental in differentiating liver lesions. The major challenge involves modeling reliable connections and simultaneously learning complementary information across various MRI sequences. While previous studies have primarily focused on multimodal integration in a pair-wise manner using few modalities, our research seeks to advance a more comprehensive understanding of interaction modeling by establishing complex high-order correlations among the diverse modalities in multimodal MRI. In this paper, we introduce a multimodal graph learning with multi-hypergraph reasoning network to capture the full spectrum of both pair-wise and group-wise relationships among different modalities. Specifically, a weight-shared encoder extracts features from regions of interest (ROI) images across all modalities. Subsequently, a collection of uniform hypergraphs are constructed with varying vertex configurations, allowing for the modeling of not only pair-wise correlations but also the high-order collaborations for relational reasoning. Following information propagation through the hypergraph message passing, adaptive intra-modality fusion module is proposed to effectively fuse feature representations from different hypergraphs of the same modality. Finally, all refined features are concatenated to prepare for the classification task. Our experimental evaluations, including focal liver lesions classification using the LLD-MMRI2023 dataset and early recurrence prediction of hepatocellular carcinoma using our internal datasets, demonstrate that our method significantly surpasses the performance of existing approaches, indicating the effectiveness of our model in handling both pair-wise and group-wise interactions across multiple modalities.

Accurate preoperative recurrence prediction for non-small cell lung cancer (NSCLC) is a challenging issue in the medical field. Existing studies primarily conduct image and molecular analyses independently or directly fuse multimodal information through radiomics and genomics, which fail to fully exploit and effectively utilize the highly heterogeneous cross-modal information at different levels and model the complex relationships between modalities, resulting in poor fusion performance and becoming the bottleneck of precise recurrence prediction. To address these limitations, we propose a novel unified framework, the Self-and-Mutual Attention (SAMA) Network, designed to efficiently fuse and utilize macroscopic CT images and microscopic gene data for precise NSCLC recurrence prediction, integrating handcrafted features, deep features, and gene features. Specifically, we design a Self-and-Mutual Attention Module that performs three-stage fusion: the self-enhancement stage enhances modality-specific features; the gene-guided and CT-guided cross-modality fusion stages perform bidirectional cross-guidance on the self-enhanced features, complementing and refining each modality, enhancing heterogeneous feature expression; and the optimized feature aggregation stage ensures the refined interactive features for precise prediction. Extensive experiments on both publicly available datasets from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) demonstrate that our method achieves state-of-the-art performance and exhibits broad applicability to various cancers.

Although contrast-enhanced computed tomography (CE-CT) images significantly improve the accuracy of diagnosing focal liver lesions (FLLs), the administration of contrast agents imposes a considerable physical burden on patients. The utilization of generative models to synthesize CE-CT images from non-contrasted CT images offers a promising solution. However, existing image synthesis models tend to overlook the importance of critical regions, inevitably reducing their effectiveness in downstream tasks. To overcome this challenge, we propose an innovative CE-CT image synthesis model called Segmentation Guided Crossing Dual Decoding Generative Adversarial Network (SGCDD-GAN). Specifically, the SGCDD-GAN involves a crossing dual decoding generator including an attention decoder and an improved transformation decoder. The attention decoder is designed to highlight some critical regions within the abdominal cavity, while the improved transformation decoder is responsible for synthesizing CE-CT images. These two decoders are interconnected using a crossing technique to enhance each other's capabilities. Furthermore, we employ a multi-task learning strategy to guide the generator to focus more on the lesion area. To evaluate the performance of proposed SGCDD-GAN, we test it on an in-house CE-CT dataset. In both CE-CT image synthesis tasks-namely, synthesizing ART images and synthesizing PV images-the proposed SGCDD-GAN demonstrates superior performance metrics across the entire image and liver region, including SSIM, PSNR, MSE, and PCC scores. Furthermore, CE-CT images synthetized from our SGCDD-GAN achieve remarkable accuracy rates of 82.68%, 94.11%, and 94.11% in a deep learning-based FLLs classification task, along with a pilot assessment conducted by two radiologists.

Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally adopt a two-stage approach, comprising a non-learnable feature embedding stage and a classifier training stage. Though it can greatly reduce memory consumption by using a fixed feature embedder pre-trained on other domains, such a scheme also results in a disparity between the two stages, leading to suboptimal classification accuracy. To address this issue, we propose that a bag-level classifier can be a good instance-level teacher. Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost. ICMIL initially fixes the patch embedder to train the bag classifier, followed by fixing the bag classifier to fine-tune the patch embedder. The refined embedder can then generate better representations in return, leading to a more accurate classifier for the next iteration. To realize more flexible and more effective embedder fine-tuning, we also introduce a teacher-student framework to efficiently distill the category knowledge in the bag classifier to help the instance-level embedder fine-tuning. Intensive experiments were conducted on four distinct datasets to validate the effectiveness of ICMIL. The experimental results consistently demonstrated that our method significantly improves the performance of existing MIL backbones, achieving state-of-the-art results. The code and the organized datasets can be accessed by: https://github.com/Dootmaan/ICMIL/tree/confidence-based.

Multimodal magnetic resonance imaging (MRI) provides complementary information about targets, and the segmentation of multimodal MRI is widely used as an essential preprocessing step for initial diagnosis, stage differentiation, and post-treatment efficacy evaluation in clinical situations. For the main modality or each of the modalities, it is important to enhance the visual information by modeling the connection and effectively fusing the features among them. However, the existing methods for multimodal segmentation have a drawback; they coincidentally drop information of individual modality during the fusion process. Recently, graph learning-based methods have been applied in segmentation, and these methods have achieved considerable improvements by modeling the relationships across feature regions and reasoning using global information. In this paper, we propose a graph learning-based approach to efficiently extract modality-specific features and establish regional correspondence effectively among all modalities. In detail, after projecting features into a graph domain and employing graph convolution to propagate information across all regions for learning global modality-specific features, we propose a mutual information-based graph co-attention module to learn the weight coefficients of one bipartite graph constructed by the fully connected graphs having different modalities in the graph domain and by selectively fusing the node features. Based on the deformation diagram between the spatial-graph space and our proposed graph co-attention module, we present a multimodal prior-guided segmentation framework, which uses two strategies for two clinical situations:Modality-Specific Learning StrategyandCo-Modality Learning Strategy. Besides, the improvedCo-Modality Learning Strategyis used with trainable weights in the multi-task loss for the optimization of the proposed framework. We validated our proposed modules and frameworks on two multimodal MRI datasets: our private liver lesion dataset and a public prostate zone dataset. Our experimental results on both datasets prove the superiority of our proposed approaches.