Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning

Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. This suggests a promising paradigm shift of adapting pre-trained ViT models to Federated Learning (FL) settings. However, the challenge of data heterogeneity among FL clients presents a significant hurdle in effectively deploying ViT models. Existing Generalized FL (GFL) and Personalized FL (PFL) methods have limitations in balancing performance across both global and local data distributions. In this paper, we present a novel algorithm, SGPT, that integrates GFL and PFL approaches by employing a unique combination of both shared and group-specific prompts. This design enables SGPT to capture both common and group-specific features. A key feature of SGPT is its prompt selection module, which facilitates the training of a single global model capable of automatically adapting to diverse local client data distributions without the need for local fine-tuning. To effectively train the prompts, we utilize block coordinate descent (BCD), learning from common feature information (shared prompts), and then more specialized knowledge (group prompts) iteratively. Theoretically, we justify that learning the proposed prompts can reduce the gap between global and local performance. Empirically, we conduct experiments on both label and feature heterogeneity settings in comparison with state-of-the-art baselines, along with extensive ablation studies, to substantiate the superior performance of SGPT

Community-Aware Transformer for Autism Prediction in fMRI Connectome

Autism spectrum disorder(ASD) is a lifelong neurodevelopmental condition that affects social communication and behavior. Investigating functional magnetic resonance imaging (fMRI)-based brain functional connectome can aid in the understanding and diagnosis of ASD, leading to more effective treatments. The brain is modeled as a network of brain Regions of Interest (ROIs), and ROIs form communities and knowledge of these communities is crucial for ASD diagnosis. On the one hand, Transformer-based models have proven to be highly effective across several tasks, including fMRI connectome analysis to learn useful representations of ROIs. On the other hand, existing transformer-based models treat all ROIs equally and overlook the impact of community-specific associations when learning node embeddings. To fill this gap, we propose a novel method, Com-BrainTF, a hierarchical local-global transformer architecture that learns intra and inter-community aware node embeddings for ASD prediction task. Furthermore, we avoid over-parameterization by sharing the local transformer parameters for different communities but optimize unique learnable prompt tokens for each community. Our model outperforms state-of-the-art (SOTA) architecture on ABIDE dataset and has high interpretability, evident from the attention module. Our code is available at https://github.com/ubc-tea/Com-BrainTF.Comment: Accepted by 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging

LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening

In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and FNAC 2019 dataset with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on a urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on FNAC 2019 dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.Comment: This paper was submitted to Medical Image Analysis. It is under revie

Associations between benign paroxysmal positional vertigo and seven mental disorders: a two-sample Mendelian randomization study

BackgroundThe association between benign paroxysmal positional vertigo (BPPV) and various mental disorders is still controversial. This study used the Mendelian randomization (MR) method to clarify the correlation between BPPV and seven mental disorders (bipolar disorder, depression, anxiety disorder, schizophrenia, suicidality, neuroticism, and mood swings) to aid in the exploration of BPPV complications and prevention and early treatment of mental disorders.MethodsThe datasets for BPPV and seven mental disorders were obtained from genome-wide association studies (GWASs). Two-sample MR was used to analyze the correlation between exposure (BPPV) and various outcomes (bipolar disorder, depression, anxiety disorder, schizophrenia, suicidality, neuroticism, and mood swings). A reverse MR study was also performed. The inverse variance weighting (IVW) method, the MR–Egger method, the simple mode method, the weighted mode method, and the weighted median method were selected.ResultsThe MR analysis and the reverse MR analysis results did not reveal significant associations between BPPV and bipolar disorder, depression, anxiety disorder, schizophrenia, suicidal tendencies, neuroticism, and mood swings. Interestingly, neuroticism (IVW: OR = 1.142, 95% CI: 1.059–1.231, P = 0.001; P-MR-PRESSO adjustment = 0.0002) and mood swings (IVW: OR = 3.119, 95% CI: 1.652–5.884, P = 0.0004) may have a significant association with BPPV. After MR-PRESSO adjustment, there was no horizontal pleiotropy or heterogeneity, and a significant association between neuroticism, mood swings, and BPPV has still been suggested.ConclusionWe conducted MR analysis on genetic data from European populations and discovered a causal relationship between BPPV and the seven mental disorders. Our research findings suggest that BPPV may not have a significant causal relationship with bipolar disorder, depression, anxiety disorder, schizophrenia, or suicidal tendencies. However, neuroticism and mood swings may be risk factors for BPPV

Design, fabrication, and cold test of a high frequency system for an H-band sheet beam travelling wave tube

The design, fabrication and cold test of a high frequency system (HFS) for an H-band (220-325GHz) sheet beam travelling wave tube is presented in this article. The HFS was composed of a 90-period double-staggered grating waveguide and a pair of identical L-shaped couplers with Bragg reflectors and matching steps. The HFS was manufactured by nano-computer numerical control machining and its electromagnetic properties were measured by using a vector network analyzer. The measured S-parameters were in good agreement with the simulated ones, which predicted a 3 dB bandwidth of ∼47.0 GHz. The maximum value of the measured transmission coefficient S 21 was -4.9 dB and the in-band port reflection S 11 was around -15.0 dB. Based on the cold testing-based HFS, simulations of the beam wave interaction predicted a stable output power of over 55.1 W in the frequency range of 230-280 GHz. In addition, the instabilities of the beam wave interaction were investigated in the simulation

Instructive Feature Enhancement for Dichotomous Medical Image Segmentation

Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance. However, existing networks tend to struggle with task-specific, heavy and complex designs to improve accuracy. They made little instructions to which feature channels would be more beneficial for segmentation, and that may be why the performance and universality of these segmentation models are hindered. In this study, we propose an instructive feature enhancement approach, namely IFE, to adaptively select feature channels with rich texture cues and strong discriminability to enhance raw features based on local curvature or global information entropy criteria. Being plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model to focus on texture-rich features which are especially important for the ambiguous and challenging boundary identification, simultaneously achieving simplicity, universality, and certain interpretability. To evaluate the proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k, which contains 55,023 images from 7 modalities and 26 anatomical structures. Extensive experiments show that IFE can improve the performance of classic segmentation networks across different anatomies and modalities with only slight modifications. Code is available at https://github.com/yezi-66/IFEComment: Accepted by MICCAI 202

Strong Electronic Interaction of Amorphous Fe2O3 Nanosheets with Single‐Atom Pt toward Enhanced Carbon Monoxide Oxidation

Platinum‐based catalysts are critical to several chemical processes, but their efficiency is not satisfying enough in some cases, because only the surface active‐site atoms participate in the reaction. Henceforth, catalysts with single‐atom dispersions are highly desirable to maximize their mass efficiency, but fabricating these structures using a controllable method is still challenging. Most previous studies have focused on crystalline materials. However, amorphous materials may have enhanced performance due to their distorted and isotropic nature with numerous defects. Here reported is the facile synthesis of an atomically dispersed catalyst that consists of single Pt atoms and amorphous Fe2O3 nanosheets. Rational control can regulate the morphology from single atom clusters to sub‐nanoparticles. Density functional theory calculations show the synergistic effect resulted from the strong binding and stabilization of single Pt atoms with the strong metal‐support interaction between the in situ locally anchored Pt atoms and Fe2O3 lead to a weak CO adsorption. Moreover, the distorted amorphous Fe2O3 with O vacancies is beneficial for the activation of O2, which further facilitates CO oxidation on nearby Pt sites or interface sites between Pt and Fe2O3, resulting in the extremely high performance for CO oxidation of the atomic catalyst.An atomically Pt dispersed catalyst on amorphous Fe2O3 nanosheets is developed. The size effect of Pt and phase effect of support are explored. The synergistic effect results from the strong metal‐support interactions between the single Pt atoms and the amorphous Fe2O3 structure supports lead to an enhanced CO oxidation performance.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151833/1/adfm201904278-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151833/2/adfm201904278.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151833/3/adfm201904278_am.pd

Boosting Oxygen and Peroxide Reduction Reactions on PdCu Intermetallic Cubes

Palladium‐based nanocatalysts have the potential to replace platinum‐based catalysts for fuel‐cell reactions in alkaline electrolytes, especially PdCu intermetallic nanoparticles with high electrochemical activity and stability. However, unlike the synthetic methods for obtaining the nanoparticles, the effect of PdCu shape on the performance is relatively less well studied. Here, we demonstrate the facet dependence of PdCu intermetallics on the oxygen reduction reaction (ORR) and peroxide reduction, and reveal that the {100} dominant PdCu cubes have a much higher ORR mass activity and specific activity than spheres at 0.9 V vs. RHE, which is four and five times that of commercial Pd/C and Pt/C catalysts, respectively, and show only a 31.7 % decay after 30 000 cycles in the stability test. Moreover, cubic PdCu nanoparticles show higher peroxide electroreduction activity than Pd cubes and PdCu spheres. Density functional theory (DFT) calculation reveals that the huge difference originates from the reduction in oxygen adsorption energy and energy barrier of peroxide decomposition on the ordered {100} PdCu surface. Given the relationship between the shape and electrochemical performance, this study will contribute to further research on electrocatalytic improvements of catalysts in alkaline environments.Shape the future: PdCu intermetallic cubes and spheres are synthesized to investigate the facet dependence on the oxygen reduction reaction and peroxide reduction. The cubes show large improvements in mass activity towards both reactions, compared with the spheres. DFT calculation uncovers that the dominant {100} faces of the cubes offer more appropriate oxygen adsorption and are thermodynamically favorable for peroxide reduction compared to the surface of spheres.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/1/celc202000381.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/2/celc202000381_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/3/celc202000381-sup-0001-misc_information.pd