Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles

In this study, we proposed and validated a multi-atlas guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions of interest (ROIs) from structural magnetic resonance images (MRIs). One major limitation of existing state-of-the-art 3D FCN segmentation models is that they often apply image patches of fixed size throughout training and testing, which may miss some complex tissue appearance patterns of different brain ROIs. To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns. In addition, with an introduction of multi-atlas based guidance in M-FCN, our segmentation was generated by combining the information of images and labels, which is highly robust. To reduce over-fitting of the FCN model on the training data, we adopted an ensemble strategy in the learning procedure. Evaluation was performed on two brain MRI datasets, aiming respectively at segmenting 14 subcortical and ventricular structures and 54 brain ROIs. The segmentation results of the proposed method were compared with those of a state-of-the-art multi-atlas based segmentation method and an existing 3D FCN segmentation model. Our results suggested that the proposed method had a superior segmentation performance

Tiazofurin inhibits oral cancer growth in vitro and in vivo via upregulation of miR-204 expression

Purpose: To investigate the effect of tiazofurin on proliferation and growth of oral cancer cells, and the associated mechanism(s) of action.Methods: The effect of tiazofurin on the cytotoxicity of SCC-VII and SCC-25 oral cancer cells were measured using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, while cell apoptosis was determined by flow cytometry. Western blotting was used for assaying proteinexpressions.Results: Tiazofurin inhibited the viability of the oral cancer cells in a concentration-based manner (p < 0.05). Tiazofurin treatment at a dose of 2.0 μM reduced the proliferation of SCC-VII and SCC-25 cells to 25 and 22 %, respectively. Apoptosis was significantly increased in SCC-VII and SCC-25 cells by tiazofurin treatment, relative to untreated cells (p < 0 .05). Tiazofurin also increased the activation levels of caspase-3 and caspase-9 and downregulated the expressions of p-Akt and p-mTOR in the two cancer cell lines. Moreover, miR-204 expression was significantly promoted in the tiazofurin-treated cells, when compared to control (p < 0 .05). In SCC-VII cells, treatment with tiazofurin suppressed Factin expression, relative to control.Conclusion: These results demonstrate that tiazofurin inhibits the viability and proliferation of SCC-VII and SCC-25 cancer cells via induction of apoptosis and activation of caspase-3/caspase-9. Moreover, tiazofurin targets Akt/mTOR pathway, and upregulats the expressions of F-actin and miR-204 in the oral carcinoma cells. These findings suggest that tiazofurin has a potential for use as an effective treatment for oral cancer. Keywords: Oral cancer, Tiazofurin, Apoptosis, Caspase, Cytotoxicit

MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging

Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability. While most explainable deep learning applications use post hoc methods (such as GradCAM) to generate feature attribution maps, there is a new type of case-based reasoning models, namely ProtoPNet and its variants, which identify prototypes during training and compare input image patches with those prototypes. We propose the first medical prototype network (MProtoNet) to extend ProtoPNet to brain tumor classification with 3D multi-parametric magnetic resonance imaging (mpMRI) data. To address different requirements between 2D natural images and 3D mpMRIs especially in terms of localizing attention regions, a new attention module with soft masking and online-CAM loss is introduced. Soft masking helps sharpen attention maps, while online-CAM loss directly utilizes image-level labels when training the attention module. MProtoNet achieves statistically significant improvements in interpretability metrics of both correctness and localization coherence (with a best activation precision of $0.713\pm0.058$) without human-annotated labels during training, when compared with GradCAM and several ProtoPNet variants. The source code is available at https://github.com/aywi/mprotonet.Comment: 15 pages, 5 figures, 1 table; accepted for oral presentation at MIDL 2023 (https://openreview.net/forum?id=6Wbj3QCo4U4); camera-ready versio

FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems

Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protecting privacy by avoiding data exchange between clients and servers, it has been shown that the server can infer user ratings based on updated non-zero gradients obtained from two consecutive rounds of user-uploaded gradients. Moreover, federated recommendation systems (FRS) face the challenge of heterogeneity, leading to decreased recommendation performance. In this paper, we propose FedRec+, an ensemble framework for FRS that enhances privacy while addressing the heterogeneity challenge. FedRec+ employs optimal subset selection based on feature similarity to generate near-optimal virtual ratings for pseudo items, utilizing only the user's local information. This approach reduces noise without incurring additional communication costs. Furthermore, we utilize the Wasserstein distance to estimate the heterogeneity and contribution of each client, and derive optimal aggregation weights by solving a defined optimization problem. Experimental results demonstrate the state-of-the-art performance of FedRec+ across various reference datasets.Comment: Accepted by 59th Annual Allerton Conference on Communication, Control, and Computin