Compound attention embedded dual channel encoder-decoder for ms lesion segmentation from brain MRI
Multiple Sclerosis (MS) lesions’ segmentation is difficult due to their variegated sizes, shapes, and intensity levels. Besides this, the class imbalance problem and the availability of limited
Multiple Sclerosis (MS) lesions’ segmentation is difficult due to their variegated sizes, shapes, and intensity levels. Besides this, the class imbalance problem and the availability of limited annotated data samples obstruct the building of highly efficient deep learning-based models. Though researchers have made many attempts to design efficient deep learning-based models, the maximum Dice Coefficient achieved by their models is fairly below the acceptable level of 0.70. The possible reason may be due to the inability to capture sufficient local and global features of the lesions required for accurate segmentation. In this paper, we present a new deep-learning architecture based on compound attention for MS lesion segmentation from magnetic resonance images that handles the challenges of capturing the local and global variable features of the MS lesions. The proposed model is equipped with a dualchannel CNN encoder-decoder structure employing residual connections in one channel and residual channel and spatial attention in the other. The residual connections alleviate the vanishing gradient problem and pass the fine-grained information through the channels, which is crucial for pixel-wise prediction. The attention mechanism used in a channel helps to capture long-range dependencies. Thus, the complete model leverages rich global and local information through the two channels for lesion segmentation. The problem of data imbalance is handled by using the Focal Tversky loss function. Through rigorous evaluation using 3-fold cross-validation on the MICCAI 2016 challenge dataset, our model demonstrates superior performance, achieving a Dice Coefficient of 0.73, surpassing state-of-the-art models in both qualitative and quantitative assessments.
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Graphically Residual Attentive Network For Tackling Aerial Image Occlusion
Deep learning has rapidly advanced, and many new applications are being developed for tasks like object detection, text recognition, occlusion handling, etc. However, chal
Deep learning has rapidly advanced, and many new applications are being developed for tasks like object detection, text recognition, occlusion handling, etc. However, chal- lenges still exist in the detection of objects in complex environments such as aerial images where things like motion blur, low light, and significant occlusion occur. This paper addresses similar challenges by introducing a novel framework, the Graphically Residual Attentive Network (GRESIDAN). GRESIDAN integrates three synergistic pipelines for object detection, occlusion detection, and occlusion removal. GRESI- DAN uses a residually attentive block combining ResNet-18 and a multi-headed atten- tion mechanism to improve feature extraction and detection accuracy in low-quality, occluded aerial images. A graphically attentive occlusion detection pipeline is imple- mented to handle occlusion, segment better, and mask out the occluder in the aerial im- age. The pipelines of the GRESIDAN model are validated on the COCO-2017 dataset and a custom private aerial object detection dataset, outperforming the state-of-the- art methods in handling occlusion and detecting objects. Our contributions provide a robust solution to the problem of detecting and handling occluded objects in aerial imagery, pushing the boundaries of automated visual recognition in challenging real- world scenarios.
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