Glioblastoma (GBM) remains a brain tumor with extremely poor prognosis, necessitating precise survival prediction to guide personalized treatment planning. Each MRI modality highlights distinct biological features of GBM, while genetic information provides crucial context for understanding tumor and progression. This complementary information offers a more comprehensive understanding of GBM. However, existing survival prediction methods, using either statistical approaches or deep learning models, often fail to capture the intricate relationships between multimodal MRI data and genetic markers, causing significant challenges to effective integration. To address these challenges, we propose a novel framework that integrates multimodal MRI and genetic information through a tailored fusion approach reflecting the distinct biological characteristics of each modality. Our method integrates multimodal MRI data and genetic information through a modality-aware fusion approach, which preserves modality-specific features and adjusts feature representations to integrate genetic information. This design achieves superior performance by modeling modality-specific features and cross-modal interactions, while incorporating genetic information to refine the feature representation for survival prediction. As a result, this framework supports clinicians in making informed, personalized treatment decisions, ultimately enhancing patient outcomes.

Volumetric fluorescence microscopy is crucial for non-invasive three-dimension (3D) visualization of biological systems but faces challenges due to anisotropic blurring caused by the point spread function (PSF). Previous methods have struggled with adapting to the diverse PSFs and have not effectively addressed their overall impacts of PSF. We propose Isotropic Diffusion Posterior Sampling (IsotropicDPS), solving isotropic reconstruction as a blind inverse problem. Our method employs two specialized score-based diffusion models, each trained on high-resolution lateral images and a diverse set of blurring PSFs. This approach enables the joint estimation of both the clean axial images and the PSF through a conditional posterior sampling strategy with a parallel reverse diffusion process. Remarkably, IsotropicDPS achieves zero-shot reconstruction and PSF estimation without requiring axial images during training. We validated our method through experiments on synthetic and real data, demonstrating superior performance and adaptability to varying PSF scenarios compared to existing methods.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used functional neuroimaging technique to investigate the functional brain networks. However, rs-fMRI data are often contaminated with noise and artifacts that adversely affect the results of rs-fMRI studies. Several machine/deep learning methods have achieved impressive performance to automatically regress the noise-related components decomposed from rs-fMRI data, which are expressed as the pairs of a spatial map and its associated time series. However, most of the previous methods individually analyze each modality of the noise-related components and simply aggregate the decision-level information (or knowledge) extracted from each modality to make a final decision. Moreover, these approaches consider only the limited modalities making it difficult to explore class-discriminative spectral information of noise-related components. To overcome these limitations, we propose a unified deep attentive spatio-spectral-temporal feature fusion framework. We first adopt a learnable wavelet transform module at the input-level of the framework to elaborately explore the spectral information in subsequent processes. We then construct a feature-level multi-modality fusion module to efficiently exchange the information from multi-modality inputs in the feature space. Finally, we design confidence-based voting strategies for decision-level fusion at the end of the framework to make a robust final decision. In our experiments, the proposed method achieved remarkable performance for noise-related component detection on various rs-fMRI datasets.