Aneurysmal subarachnoid hemorrhage (SAH) is a life-threatening neurological emergency with mortality rates exceeding 30%. Transfer learning from related hematoma types represents a potentially valuable but underexplored approach. Although Unet architectures remain the gold standard for medical image segmentation due to their effectiveness on limited datasets, Low-Rank Adaptation (LoRA) methods for parameter-efficient transfer learning have been rarely applied to convolutional neural networks in medical imaging contexts. We implemented a Unet architecture pre-trained on computed tomography scans from 124 traumatic brain injury patients across multiple institutions, then fine-tuned on 30 aneurysmal SAH patients from the University of Michigan Health System using 3-fold cross-validation. We developed a novel CP-LoRA method based on tensor CP-decomposition and introduced DoRA variants (DoRA-C, convDoRA, CP-DoRA) that decompose weight matrices into magnitude and directional components. We compared these approaches against existing LoRA methods (LoRA-C, convLoRA) and standard fine-tuning strategies across different modules on a multi-view Unet model. LoRA-based methods consistently outperformed standard Unet fine-tuning. Performance varied by hemorrhage volume, with all methods showing improved accuracy for larger volumes. CP-LoRA achieved comparable performance to existing methods while using significantly fewer parameters. Over-parameterization with higher ranks consistently yielded better performance than strictly low-rank adaptations. This study demonstrates that transfer learning between hematoma types is feasible and that LoRA-based methods significantly outperform conventional Unet fine-tuning for aneurysmal SAH segmentation.

Virtual Try-On (VTON) is the task of synthesizing an image of a person wearing a target garment, conditioned on a person image and a garment image. While diffusion-based VTON models featuring a Dual UNet architecture demonstrate superior fidelity compared to single UNet models, they incur substantial computational and memory overhead due to their heavy structure. In this study, through visualization analysis and theoretical analysis, we derived three hypotheses regarding the learning of context features to condition the denoising process. Based on these hypotheses, we developed Re-CatVTON, an efficient single UNet model that achieves high performance. W e further enhance the model by introducing a modified classifier-free guidance strategy tailored for VTON's spatial concatenation conditioning, and by directly injecting the ground-truth garment latent derived from the clean garment latent to prevent the accumulation of prediction error . The proposed Re-CatVTON significantly improves performance compared to its predecessor (CatVTON) and requires less computation and memory than the high-performance Dual UNet model, Leffa. Our results demonstrate improved FID, KID, and LPIPS scores, with only a marginal decrease in SSIM, establishing a new efficiency-performance trade-off for single UNet VTON models.

We present a robust neural watermarking framework for scientific data integrity, targeting high-dimensional fields common in climate modeling and fluid simulations. Using a convolutional autoencoder, binary messages are invisibly embedded into structured data such as temperature, vorticity, and geopotential. Our method ensures watermark persistence under lossy transformations - including noise injection, cropping, and compression - while maintaining near-original fidelity (sub-1\% MSE). Compared to classical singular value decomposition (SVD)-based watermarking, our approach achieves $>$98\% bit accuracy and visually indistinguishable reconstructions across ERA5 and Navier-Stokes datasets. This system offers a scalable, model-compatible tool for data provenance, auditability, and traceability in high-performance scientific workflows, and contributes to the broader goal of securing AI systems through verifiable, physics-aware watermarking. We evaluate on physically grounded scientific datasets as a representative stress-test; the framework extends naturally to other structured domains such as satellite imagery and autonomous-vehicle perception streams.

These authors contributed equally to this work. Abstract Micromagnetics has made significant strides, particularly due to its wide-ranging applications in magnetic storage design and the recent exciting advancements in spintronics research. Numerical simulation is a cornerstone of micromagnetics research, relying on first-principle rules to compute the dynamic evolution of micromagnetic systems based on the renowned LLG equation, named after Landau, Lifshitz, and Gilbert. However, simulations are often hindered by their slow speed, primarily due to the global convolution required to compute the demagnetizing field, which involves full interaction among any two units in the sample. Although Fast-Fourier transformation (FFT) calculations reduce the computational complexity to O(NlogN), it remains impractical for large-scale simulations. In this paper, we introduce NeuralMAG, a deep learning approach to micromagnetic simulation. Our innovative approach follows the LLG iterative framework but accelerates demagnetizing field computation through the employment of a U-shaped neural network (Unet). The Unet architecture comprises an encoder that extracts aggregated spins at various scales and learns the local interaction at each scale, followed by a decoder that accumulates the local interactions at different scales to approximate the global convolution. This divide-and-accumulate scheme achieves a time complexity of O(N), significantly enhancing the speed and feasibility of large-scale simulations. To validate the new approach, we trained a single model and evaluated it on two micromagnetics tasks with various sample sizes, shapes, and material settings: (1) basic LLG dynamic evolution, and (2) MH curve estimation. The results show that the model maintains reasonable accuracy and is significantly faster than the conventional FFT-based method, achieving a sixfold speedup for large-size models. NeuralMAG has been published online and is available for users to download. Born in the early 20th century to address the issues of magnetic domain and hysteresis, micromagnetics has evolved into the fundamental methodology for understanding the magnetic behavior of materials from a microscopic view [1, 2].

An improved model of medical image segmentation for brain tumor is discussed, which is a deep learning algorithm based on U-Net architecture. Based on the traditional U-Net, we introduce GSConv module and ECA attention mechanism to improve the performance of the model in medical image segmentation tasks. With these improvements, the new U-Net model is able to extract and utilize multi-scale features more efficiently while flexibly focusing on important channels, resulting in significantly improved segmentation results. During the experiment, the improved U-Net model is trained and evaluated systematically. By looking at the loss curves of the training set and the test set, we find that the loss values of both rapidly decline to the lowest point after the eighth epoch, and then gradually converge and stabilize. This shows that our model has good learning ability and generalization ability. In addition, by monitoring the change in the mean intersection ratio (mIoU), we can see that after the 35th epoch, the mIoU gradually approaches 0.8 and remains stable, which further validates the model. Compared with the traditional U-Net, the improved version based on GSConv module and ECA attention mechanism shows obvious advantages in segmentation effect. Especially in the processing of brain tumor image edges, the improved model can provide more accurate segmentation results. This achievement not only improves the accuracy of medical image analysis, but also provides more reliable technical support for clinical diagnosis.

Automated cell segmentation is crucial for various biological and medical applications, facilitating tasks like cell counting, morphology analysis, and drug discovery. However, manual segmentation is time-consuming and prone to subjectivity, necessitating robust automated methods. This paper presents open-source infrastructure, utilizing the UNet model, a deep-learning architecture noted for its effectiveness in image segmentation tasks. This implementation is integrated into the open-source DeepChem package, enhancing accessibility and usability for researchers and practitioners. The resulting tool offers a convenient and user-friendly interface, reducing the barrier to entry for cell segmentation while maintaining high accuracy.

Medical image segmentation plays a vital role in clinic disease diagnosis and medical image analysis. However, labeling medical images for segmentation task is tough due to the indispensable domain expertise of radiologists. Furthermore, considering the privacy and sensitivity of medical images, it is impractical to build a centralized segmentation dataset from different medical institutions. Federated learning aims to train a shared model of isolated clients without local data exchange which aligns well with the scarcity and privacy characteristics of medical data. To solve the problem of labeling hard, many advanced semi-supervised methods have been proposed in a centralized data setting. As for federated learning, how to conduct semi-supervised learning under this distributed scenario is worth investigating. In this work, we propose a novel federated semi-supervised learning framework for medical image segmentation. The intra-client and inter-client consistency learning are introduced to smooth predictions at the data level and avoid confirmation bias of local models. They are achieved with the assistance of a Variational Autoencoder (VAE) trained collaboratively by clients. The added VAE model plays three roles: 1) extracting latent low-dimensional features of all labeled and unlabeled data; 2) performing a novel type of data augmentation in calculating intra-client consistency loss; 3) utilizing the generative ability of itself to conduct inter-client consistency distillation. The proposed framework is compared with other federated semi-supervised or self-supervised learning methods. The experimental results illustrate that our method outperforms the state-of-the-art method while avoiding a lot of computation and communication overhead.

Introduction: Fetal resting-state functional magnetic resonance imaging (rs-fMRI) is a rapidly evolving field that provides valuable insight into brain development before birth. Accurate segmentation of the fetal brain from the surrounding tissue in nonstationary 3D brain volumes poses a significant challenge in this domain. Current available tools have 0.15 accuracy. Aim: This study introduced a novel application of artificial intelligence (AI) for automated brain segmentation in fetal brain fMRI, magnetic resonance imaging (fMRI). Open datasets were employed to train AI models, assess their performance, and analyze their capabilities and limitations in addressing the specific challenges associated with fetal brain fMRI segmentation. Method: We utilized an open-source fetal functional MRI (fMRI) dataset consisting of 160 cases (reference: fetal-fMRI - OpenNeuro). An AI model for fMRI segmentation was developed using a 5-fold cross-validation methodology. Three AI models were employed: 3D UNet, VNet, and HighResNet. Optuna, an automated hyperparameter-tuning tool, was used to optimize these models. Results and Discussion: The Dice scores of the three AI models (VNet, UNet, and HighRes-net) were compared, including a comparison between manually tuned and automatically tuned models using Optuna. Our findings shed light on the performance of different AI models for fetal resting-state fMRI brain segmentation. Although the VNet model showed promise in this application, further investigation is required to fully explore the potential and limitations of each model, including the HighRes-net model. This study serves as a foundation for further extensive research into the applications of AI in fetal brain fMRI segmentation.

Image segmentation is a branch of computer vision that is widely used in real world applications including biomedical image processing. With recent advancement of deep learning, image segmentation has achieved at a very high level performance. Recently, UNet architecture is found as the core of novel deep learning segmentation methods. In this paper we implement UNet architecture from scratch with using basic blocks in Pytorch and evaluate its performance on multiple biomedical image datasets. We also use transfer learning to apply novel modified UNet segmentation packages on the biomedical image datasets. We fine tune the pre-trained transferred model with each specific dataset. We compare its performance with our fundamental UNet implementation. We show that transferred learning model has better performance in image segmentation than UNet model that is implemented from scratch.

The main purpose of this study is to develop a pipeline for COVID-19 detection from a big and challenging database of Computed Tomography (CT) images. The proposed pipeline includes a segmentation part, a lung extraction part, and a classifier part. Optional slice removal techniques after UNet-based segmentation of slices were also tried. The methodologies tried in the segmentation part are traditional segmentation methods as well as UNet-based methods. In the classification part, a Convolutional Neural Network (CNN) was used to take the final diagnosis decisions. In terms of the results: in the segmentation part, the proposed segmentation methods show high dice scores on a publicly available dataset. In the classification part, the results were compared at slice-level and at patient-level as well. At slice-level, methods were compared and showed high validation accuracy indicating efficiency in predicting 2D slices. At patient level, the proposed methods were also compared in terms of validation accuracy and macro F1 score on the validation set. The dataset used for classification is COV-19CT Database. The method proposed here showed improvement from our precious results on the same dataset. In Conclusion, the improved work in this paper has potential clinical usages for COVID-19 detection and diagnosis via CT images. The code is on github at https://github.com/IDU-CVLab/COV19D_3rd