Related studies have revealed that the phonological features of depressed patients are different from those of healthy individuals. With the increasing prevalence of depression, an objective and convenient approach for early screening is necessary. To this end, we propose an automatic depression detection method based on hybrid speech features extracted by deep learning, dubbed as TTFNet. Firstly, to effectively excavate the intrinsic relationship among multidimensional dynamic features in the frequency domain, the log-Mel spectrogram of raw speech and its related derivatives are encoded into quaternion representation. Then, the innovatively designed quaternion VisionLSTM is utilized to capture their synergistic effects. Simultaneously, we integrate sLSTM with the pre-trained wav2vec 2.0 model to fully acquire the temporal features. In addition, to further exploit the complementarity between temporal and frequency features, we design an XConformer block for cross-sequence interactions, which ingeniously combines self-attention mechanisms and convolutional modules. Based on this block, the dual-path fusion module closely utilizes the mutual promotion of features from different domains, thereby enhancing generalization capability of the proposed model. Extensive experiments conducted on the AVEC 2013, AVEC 2014, DAIC-WOZ and E-DAIC datasets demonstrate that our method outperforms current state-of-the-art methods in both depression recognition and severity prediction tasks.

Image fusion is an important branch of medical image processing and analysis. The main steps of image fusion are: acquiring and preprocessing data from source images, extracting and selecting relevant features, choosing suitable fusion methods and algorithms, generating fused results, and evaluating them. While significant progress has been made in feature extraction and reconstruction, the fusion strategy remains an ongoing exploration. Therefore, we propose an innovative fusion strategy, namely the Three-Branch SR2Fusion Strategy. We adopt a two-stage training approach, initially training an encoder and a nested-connected decoder as an auto-encoder to extract multi-modal deep features. Subsequently, our trained SR2fusion network integrates the extracted deep features. The initialized features are fed into a Separation Reconstruction Unit (SRU) network, branching into two pathways: a channel attention network and a convolutional neural network. This is designed to reduce the redundancy of spatial features while preserving the structural information of the source images. Also, the initialized features from MRI and PET images are concatenated and fed into the third branch. In addition, to prevent feature loss and retain more detailed information, we introduce three recurrent residual modules. Finally, the features obtained from three branches are aggregated and the decoder is employed to reconstruct the ultimate fused images. The experimental results conducted on the public dataset demonstrate the superiority of our approach in both subjective and objective evaluations compared to other advanced methods.

Traditional fusion approaches and most deep learning-based methods usually generate the intermediate decision map, resulting in detail loss of source images or fusion results. In this work, to enhance the detailed features and structured information from source images, we propose a dual cascade attention network (DCAN) to obtain a more informative fusion image for PET and MRI images. In our approach, channel attention is employed to improve the ability of features representation and spatial attention can highlight informative regions in the proposed fusion network. Additionally, channel and spatial attention are sequential arrangement in channel-first. Moreover, to achieve good performance in the procedure of feature extraction and image reconstruction, two-stage training strategy is adopted to train our fusion model. Experimental results demonstrate that the proposed approach achieves remarkable performance for PET and MRI images fusion.

Corona Virus Disease 2019 (COVID-19) spread globally in early 2020, leading to a new health crisis. Automatic segmentation of lung infections from computed tomography (CT) images provides an important basis for early diagnosis of COVID-19 quickly. In this paper, we propose an effective COVID-19 Lung Infection Segmentation Network (LISNet) based on edge supervision and multi-scale context aggregation. More specifically, an Edge Supervision module is introduced to the feature extraction part to enhance the low contrast between lesions and normal tissues. In addition, the Multi-scale Feature Fusion module is added to enhance the segmentation ability of different scales Lesions. Finally, the Context Aggregation module is used to aggregate high- and low-level features and generate global information. Experiments demonstrate that our method outperforms other state-of-the-art methods on the public COVID-19 CT segmentation dataset.

In this paper, we propose a novel fusion framework based on a dense network with channel and spatial attention (CSpA-DN) for PET and MR images. In our approach, an encoder composed of the densely connected neural network is constructed to extract features from source images, and a decoder network is leveraged to yield the fused image from these features. Simultaneously, a self-attention mechanism is introduced in the encoder and decoder to further integrate local features along with their global dependencies adaptively. The extracted feature of each spatial position is synthesized by a weighted summation of those features at the same row and column with this position via a spatial attention module. Meanwhile, the interdependent relationship of all feature maps is integrated by a channel attention module. The summation of the outputs of these two attention modules is fed into the decoder and the fused image is generated. Experimental results illustrate the superiorities of our proposed CSpA-DN model compared with state-of-the-art methods in PET and MR images fusion according to both visual perception and objective assessment.

A new divergence measure for rigid image registration is proposed that uses the properties of the Arimoto entropy. This Jensen-Arimoto divergence allows designing a novel registration method by minimizing a dissimilarity measure through the steepest gradient descent optimization method. Preliminary experiments on simulated magnetic resonance images with partial overlap and different degrees of noise have been carried out and a comparison has been conducted with other relevant information theoretic measures such as the normalized mutual information and the cross cumulative residual entropy. The results show that the proposed registration approach has better robustness to noise and can provide better registration accuracy, i.e. a sub pixel accuracy less than 0.1mm and 0.1 degree for translation and rotation. In addition, the calculation time for a 2D rigid registration is improved by approximately 10-20 % compared to the other two methods.