The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease-relevant findings similar to those in the X-ray images and by refining generated reports. In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive learning. This approach ensures the retrieval of reports with similar disease-relevant findings that closely align with the input X-ray images. In the second stage, we further enhance the initial reports by introducing a self-correction module that re-aligns them with the X-ray images. Our proposed framework achieves state-of-the-art results on two widely used benchmarks, surpassing previous approaches in both report generation and clinical efficacy metrics, thereby enhancing the trustworthiness of radiology reports.

Two-photon calcium imaging is a powerful technique for recording neuronal activities over extended periods. However, reliably capturing neuronal signals from the this data poses a significant challenge due to non-uniform neuropil distribution and densely packed neuronal populations. In this study, we leverage deep learning (DL) techniques to directly capture true neuronal signals from calcium imaging data, addressing these challenges effectively. Utilizing publicly available datasets, we demonstrate that our DL-based approach, which directly extracts neuronal signals from images, outperforms existing non-DL methods in accurately capturing neuronal signals. This highlights the significant potential of DL methods for unveiling neuronal activity hidden in calcium imaging data. Furthermore, we investigate the impact of calcium imaging data quality on the performance of DL models. While our approach demonstrates significant promise, ongoing improvements in the quality of calcium imaging data will further enhance DL techniques, leading to a deeper understanding of brain mechanisms.

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.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Major Depressive Disorder (MDD) is a pervasive disorder affecting millions of individuals, presenting a significant global health concern. Functional connectivity (FC) derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) serves as a crucial tool in revealing functional connectivity patterns associated with MDD, playing an essential role in precise diagnosis. However, the limited data availability of FC poses challenges for robust MDD diagnosis. To tackle this, some studies have employed Deep Neural Networks (DNN) architectures to construct Generative Adversarial Networks (GAN) for synthetic FC generation, but this tends to overlook the inherent topology characteristics of FC. To overcome this challenge, we propose a novel Graph Convolutional Networks (GCN)-based Conditional GAN with Class-Aware Discriminator (GC-GAN). GC-GAN utilizes GCN in both the generator and discriminator to capture intricate FC patterns among brain regions, and the class-aware discriminator ensures the diversity and quality of the generated synthetic FC. Additionally, we introduce a topology refinement technique to enhance MDD diagnosis performance by optimizing the topology using the augmented FC dataset. Our framework was evaluated on publicly available rs-fMRI datasets, and the results demonstrate that GC-GAN outperforms existing methods. This indicates the superior potential of GCN in capturing intricate topology characteristics and generating high-fidelity synthetic FC, thus contributing to a more robust MDD diagnosis.