Diffusion probabilistic models (DPMs) have recently demonstrated promising performance in medical image segmentation. However, traditional DPM has difficulty handling the irregular structure of images and the inherent similarity between lesions and surrounding tissues. To overcome these challenges, we propose an innovative architecture, the multiscale Mamba DPM (MsM-DPM), designed to enhance medical image segmentation. Specifically, MsM-DPM introduces a multiscale attention fusion module (MSAFM) in a multiscale denoising UNet (Ms-DU) to capture lesion deformations from multilevel features, thereby enhancing the model's robustness to shape and scale variations. Furthermore, in the segmentation network, a multilayer axial feature module (MLAFM) is used to adaptively aggregate the global context features from the Mamba encoder to enhance the expression of features in the spatial dimension by capturing axial multiscale features. The multilevel global context (MLGC) module is then used to reconstruct skip connections using graph convolutional network inference, and the enhanced features are assigned to each layer in the decoder to capture the contextual relationship of features. Finally, the feature fusion module (FFM) integrates deep features with upsampled features in the decoder, enhancing the network's ability to capture lesion boundary details. Our MsM-DPM effectively encodes the semantic difference between lesions and background to improve the representation of their internal features. Extensive experiments on six datasets, LUNA16, ATM22, COVID-19, Self-collected datasets, Pancreas, and BT-MSD, show that the proposed MsM-DPM outperforms existing segmentation methods. Our code is publicly available at https://github.com/suhuaqiang/deep-learning.

Image fusion technology aims to obtain a comprehensive image containing a specific target or detailed information by fusing data of different modalities. However, many deep learning-based algorithms consider edge texture information through loss functions instead of specifically constructing network modules. The influence of the middle layer features is ignored, which leads to the loss of detailed information between layers. In this article, we propose a multidiscriminator hierarchical wavelet generative adversarial network (MHW-GAN) for multimodal image fusion. First, we construct a hierarchical wavelet fusion (HWF) module as the generator of MHW-GAN to fuse feature information at different levels and scales, which avoids information loss in the middle layers of different modalities. Second, we design an edge perception module (EPM) to integrate edge information from different modalities to avoid the loss of edge information. Third, we leverage the adversarial learning relationship between the generator and three discriminators for constraining the generation of fusion images. The generator aims to generate a fusion image to fool the three discriminators, while the three discriminators aim to distinguish the fusion image and edge fusion image from two source images and the joint edge image, respectively. The final fusion image contains both intensity information and structure information via adversarial learning. Experiments on public and self-collected four types of multimodal image datasets show that the proposed algorithm is superior to the previous algorithms in terms of both subjective and objective evaluation.

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network’s ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16.

Age estimation from a single facial image is an attractive and challenging research topic in the computer vision community. Most of previous works conventionally estimate absolute age from the input face images. However, telling someone's precise age at a glance without any reference information is essentially difficult even for humans. In this paper, we propose a novel Deep Contrastive Network (DCN) for age estimation, which can mine the variation information of samples. DCN model is trained end-to-end to learn the age distances between input unseen face and several reference images by contrasting deep feature maps. In the test phase, the input image is compared with a set of selected references to determine how many years younger or older than each of baselines which is more conform with human cognitive processes. We also propose a Cyclic Iterative Approximation Algorithm (CIAA) for post age voting which can further improve the accuracy. Therefore, the age estimation problem is cast as a metric learning task in our DCN model. By jointly learned with the cost sensitive loss and KL divergence loss, our DCN is easy to train and has very stable convergence. Extensive experiments show that the proposed approach significantly outperforms other state-of-the-art age estimation methods on MORPH II and FG-NET datasets.

Parkinson’s disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient’s motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the${l}_{\mathrm {2,p}}$norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson’s Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.

Age estimation is a hot and challenging research topic in the computer vision community. Several facial datasets annotated with age and gender attributes became available in recent years. However, the statistical information of these datasets reveal the unbalanced label distribution which inevitably introduce bias during model training. In this work, we manually collect and label a large-scale age dataset called Real Scenario Face Age Dataset (RSFAD) which contains 85,044 facial images captured from surveillance cameras in the wild. Due to the COVID-19, we not only label the apparent age group and gender but also label the breathing mask, and the label distribution of RSFAD dataset is almost uniform which is the first age dataset to the best of our knowledge. In addition, we investigate the impact of age, gender and mask distribution on age group estimation by comparing GDEX CNN model trained on several different datasets. Our experiments show that the RSFAD dataset has good performance for age estimation task and also it is suitable for being an evaluation benchmark.

Age estimation from a single facial image is a challenging and attractive research area in the computer vision community. Several facial datasets annotated with age and gender attributes became available in the literature. However, one major drawback is that these datasets do not consider the label distribution during data collection. Therefore, the models training on these datasets inevitably have bias for the age having least number of images. In this work, we analyze the age and gender distribution of previous datasets and publish an Uniform Age and Gender Dataset (UAGD) which has almost equal number of female and male images in each age. In addition, we investigate the impact of age and gender distribution on age estimation by comparing DEX CNN model trained on several different datasets. Our experiments show that UAGD dataset has good performance for age estimation task and also it is suitable for being an evaluation benchmark.

Graphics processing units (GPUs) have become extremely important devices for accelerating computing performance in many applications. However, there have been few accurate models to estimate the performance of such applications running on modern GPUs. In this paper, we propose a performance model to estimate the execution times for massively parallel programs running on NVIDIA GPUs, one that takes on-chip resources and cost of data transfer between CPU and GPU into consideration. Four different GPUs with different architectures were used to evaluate our model. We demonstrated the effectiveness of the proposed model by applying it to various tasks in medical image registration. Experiments have demonstrated that by capturing on-chip GPU resources and data transfer time with our model, we were able to obtain a more accurate prediction of the actual running time, compared to the traditional model. Moreover, by using the optimal value of the block size parameter, estimated by our model, to accelerate the landmark tracking task on GPU devices, speedups of approximately 80×, 100×, 200× and 800×, on the C2050, K20c, M5000 and P100 can be achieved, making it possible to track massive numbers of landmarks and thereby improving the registration accuracy.

We propose a new method to solve the point matching problem by l1 regularization. The non-rigid transformation function based on compact support radial basis functions (CSRBF) is represented by the linear system with respect to its coefficients. The transformation function is estimated by the proposed sparse optimization model with regularizing the CSRBF coefficients by l1 norm and the affine coefficients by the square of l2 norm. The optimization model for linear problem of transformation function can be efficiently solved by a fast iterative shrinkage-thresholding algorithm (FISTA) to accelerate the convergence speed of iterative procedure. Experiments on simulated point sets and lung datasets show that our method by l1 regularization obtains accurate registration results and is robust to estimate the correspondence and the transformation between two point sets in the presence of noise and outlier.