Deformable image registration is essential in medical applications, as accurately estimating organ displacements across respiratory phases enables precise radiation dose planning in dynamic environments, mitigates damage to organs at risk (OARs), and thus improves patients' health-related quality of life. Although current learning-based methods have achieved impressive performance in small deformation registration, challenges remain due to their limited ability to capture large deformations occurring during respiration. To address this issue, we propose a novel Mamba-based hierarchical registration framework that effectively extracts both global and local features for accurate deformation prediction. Specifically, given a pair of source and target 3DCT volumes, we incorporate a foundation model pretrained on medical image registration tasks to enhance alignment accuracy. We further propose a directional-deformable Mamba scheme to facilitate global context extraction and local motion awareness. The directional Mamba component scans input features from multiple orientations to achieve broad contextual perception, while the deformable Mamba module employs adaptive directional scanning strategies to capture dynamic local variations. To overcome the scarcity of annotated respiratory data, we also collect a new respiratory lung cancer dataset comprising 100 annotated phases from 20 patients. Experimental results on our in-house dataset demonstrate that our method outperforms state-of-the-art approaches, achieving a 1.3 % improvement in overall Dice accuracy and a 1.6 dB increase in PSNR, underscoring its strong potential for clinical deployment. Code and test data are available at: https://github.com/yangyangshi806/Mamba_based_Registration.

Given the capability of multimodal data to provide information from multiple perspectives, it is beneficial for improving the accuracy of Alzheimer’s disease (AD) classification. However, during practical multimodal learning, there is a phenomenon where certain modalities dominate the decision-making, leading to insufficient learning from other modalities. Moreover, redundant information within multimodal data can also hinder accurate classification decisions. Therefore, we propose a robust multimodal representation learning method for AD classification. Specifically, we first construct dedicated encoders for each multimodal data, including structural Magnetic Resonance Imaging (sMRI) images, Positron Emission Tomography (PET) images, and Mini-Mental State Examination (MMSE) scores, to extract their respective representations. Then, we employ the information bottleneck (IB) theory to guide the model to retain classification-related information in multimodal representations while reducing redundancy among modalities. Furthermore, to promote a balanced fusion of multimodal data, we redefine the classification confidence of each modality’s representation using an orthogonal weight classifier and then introduce a regularization term to amplify the prediction score differences for modalities with lower confidence. The experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our method enhances the robustness of multimodal representations and achieves promising performance in AD-related classification tasks.

The state of the art analog circuit design applies mainly a full-custom layout methodology. This demands high expertise and heavy manual workload. Additionally, neither can the resulting layout be re-used easily across different designs or different PDKs. Learning from digital standard cells, existing work has proposed stem cells that are abuttable. But stem cells have a fixed area ratio of 2 over same-sized Pcells, limiting its wide application. In this paper we develop a new type of abuttable analog cells (called Acells) for transistors and passive elements. Acells are compatible with digital standard cells and can be abutted in all directions, enabling the use of automatic digital place and route (PnR) engines. We automate Acell generation and show that the average area ratio over same-sized Pcell is 1.49 for 65nm technology and 1.3 for 28nm technology, and is expected to decrease for more advanced technologies. We then use digital PnR to automatically layout a number of analog and mixed-signal (AMS) circuits mainly in 28nm, and show that compared to Pcell-based manual layout, Acell-based layout obtains similar performance and its circuit level layout area is about 2% higher for large scale AMS circuits in our experiments.

In this paper, we propose a wavelet-aware transformer network (WATNet) for multi-contrast knee MRI super-resolution. Unlike conventional image domain-based super-resolution methods that can not explicitly model the lost high-frequency information, our WATNet endeavors to adaptively fuse the complementary frequency information of the multi-contrast image in the wavelet domain and further refine it in the image domain. The proposed WATNet consists of the multi-scale wavelet transformation (MSWT) module, wavelet-aware transformer (WAT) module, and reconstruction (Rec) module. Specifically, the MSWT module learns to transform the MR image to multi-scale wavelet domain features by the wavelet transformation. The WAT module can adaptively search and transfer similar wavelet domain reference information to the low-resolution one. The Rec module can restore high-quality images in the image domain. To further capture more high-frequency details, we also design the wavelet-based high-frequency loss. The qualitative and quantitative experimental results indicate that our proposed WATNet outperforms most state-of-the-art methods.