Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition

BACKGROUND

Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function.

METHODS

Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification.

RESULTS

This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model.

CONCLUSION

The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.

Towards performance-maximizing neural network pruning via global channel attention

Network pruning has attracted increasing attention recently for its capability of transferring large-scale neural networks (e.g., CNNs) into resource-constrained devices. Such a transfer is typically achieved by removing redundant network parameters while retaining its generalization performance in a static or dynamic manner. Concretely, static pruning usually maintains a larger and fit-to-all (samples) compressed network by removing the same channels for all samples, which cannot maximally excavate redundancy in the given network. In contrast, dynamic pruning can adaptively remove (more) different channels for different samples and obtain state-of-the-art performance along with a higher compression ratio. However, since the system has to preserve the complete network information for sample-specific pruning, the dynamic pruning methods are usually not memory-efficient. In this paper, our interest is to explore a static alternative, dubbed GlobalPru, from a different perspective by respecting the differences among data. Specifically, a novel channel attention-based learn-to-rank framework is proposed to learn a global ranking of channels with respect to network redundancy. In this method, each sample-wise (local) channel attention is forced to reach an agreement on the global ranking among different data. Hence, all samples can empirically share the same ranking of channels and make the pruning statically in practice. Extensive experiments on ImageNet, SVHN, and CIFAR-10/100 demonstrate that the proposed GlobalPru achieves superior performance than state-of-the-art static and dynamic pruning methods by significant margins.

An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury

Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflammation is tedious, error-prone, and biased. Using the multi-parametric MR data from a rodent model study, we demonstrate the effectiveness of an end-end deep learning global-attention-based UNet (GA-UNet) framework for automatic segmentation and quantification of TBI lesions. Longitudinal MR scans (2 h, 1, 3, 7, 14, 30, and 60 days) were performed on eight Sprague-Dawley rats after controlled cortical injury was performed. TBI lesion and sub-regions segmentation was performed using 3D-UNet and GA-UNet. Dice statistics (DSI) and Hausdorff distance were calculated to assess the performance. MR scan variations-based (bias, noise, blur, ghosting) data augmentation was performed to develop a robust model.Training/validation median DSI for U-Net was 0.9368 with T2w and MPRAGE inputs, whereas GA-UNet had 0.9537 for the same. Testing accuracies were higher for GA-UNet than U-Net with a DSI of 0.8232 for the T2w-MPRAGE inputs.Longitudinally, necrosis remained constant while oligemia and penumbra decreased, and edema appearing around day 3 which increased with time. GA-UNet shows promise for multi-contrast MR image-based segmentation/quantification of TBI in large cohort studies.

A parallel network utilizing local features and global representations for segmentation of surgical instruments

PURPOSE

Automatic image segmentation of surgical instruments is a fundamental task in robot-assisted minimally invasive surgery, which greatly improves the context awareness of surgeons during the operation. A novel method based on Mask R-CNN is proposed in this paper to realize accurate instance segmentation of surgical instruments.

METHODS

A novel feature extraction backbone is built, which could extract both local features through the convolutional neural network branch and global representations through the Swin-Transformer branch. Moreover, skip fusions are applied in the backbone to fuse both features and improve the generalization ability of the network.

RESULTS

The proposed method is evaluated on the dataset of MICCAI 2017 EndoVis Challenge with three segmentation tasks and shows state-of-the-art performance with an mIoU of 0.5873 in type segmentation and 0.7408 in part segmentation. Furthermore, the results of ablation studies prove that the proposed novel backbone contributes to at least 17% improvement in mIoU.

CONCLUSION

The promising results demonstrate that our method can effectively extract global representations as well as local features in the segmentation of surgical instruments and improve the accuracy of segmentation. With the proposed novel backbone, the network can segment the contours of surgical instruments' end tips more precisely. This method can provide more accurate data for localization and pose estimation of surgical instruments, and make a further contribution to the automation of robot-assisted minimally invasive surgery.

A novel M-SegNet with global attention CNN architecture for automatic segmentation of brain MRI

In this paper, we propose a novel M-SegNet architecture with global attention for the segmentation of brain magnetic resonance imaging (MRI). The proposed architecture consists of a multiscale deep network at the encoder side, deep supervision at the decoder side, a global attention mechanism, different sizes of convolutional kernels, and combined-connections with skip connections and pooling indices. The multiscale side input layers were used to support deep layers for extracting the discriminative information and the upsampling layer at the decoder side provided deep supervision, which reduced the gradient problem. The global attention mechanism is utilized to capture rich contextual information in the decoder stage by integrating local features with their respective global dependencies. In addition, multiscale convolutional kernels of different sizes were used to extract abundant semantic features from brain MRI scans in the encoder and decoder modules. Moreover, combined-connections were used to pass features from the encoder to the decoder path to recover the spatial information lost during downsampling and makes the model converge faster. Furthermore, we adopted uniform non-overlapping input patches to focus on fine details for the segmentation of brain MRI. We verified the proposed architecture on publicly accessible datasets for the task of segmentation of brain MRI. The experimental results show that the proposed model outperforms conventional methods by achieving an average Dice similarity coefficient score of 0.96.

The visual kinetic depth effect is altered with Parkinson's disease

BACKGROUND

People with Parkinson's disease (PD) often have visual-perceptual disorders. The goal of this study was to learn if they can develop a three dimensional (3D) percept that depends on the kinetic depth effect; that is, the viewer's ability to spatially integrate over time images that are moving along many trajectories.

METHODS

Sixteen patients with PD and 12 healthy matched controls were presented with stimuli that were comprised of a circular region of randomly placed dots that moved as orthographic projections of a sphere. With a normal kinetic depth effect, the Training stimuli appear as an opaque rotating ball and the Test stimuli appear as a rotating transparent ball.

RESULTS

Whereas all controls and all PD patients reported seeing the Training stimuli as a rotating ball, the patients with PD were significantly less likely to report the Test stimuli appearing as a 3D "ball" than were the healthy participants. Instead, seven PD patients often reported these bidirectional stimuli appeared "flat."

CONCLUSIONS

This study has revealed that some patients with PD have impaired spatio-temporal integration of bidirectional visual motions, but the mechanism accounting for this loss, as well as why only some patients had this deficit, needs further study. When the driver of a moving vehicle fixates upon a stationary target in the surroundings, bidirectional retinal image motions may occur. Failure to perceive 3D structure in such moving scenes can be plausibly suspected to contribute to adverse events such as auto accidents.