14 research outputs found
Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification
Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information
Visible-infrared person re-identification (ReID) aims to recognize a same
person of interest across a network of RGB and IR cameras. Some deep learning
(DL) models have directly incorporated both modalities to discriminate persons
in a joint representation space. However, this cross-modal ReID problem remains
challenging due to the large domain shift in data distributions between RGB and
IR modalities. % This paper introduces a novel approach for a creating
intermediate virtual domain that acts as bridges between the two main domains
(i.e., RGB and IR modalities) during training. This intermediate domain is
considered as privileged information (PI) that is unavailable at test time, and
allows formulating this cross-modal matching task as a problem in learning
under privileged information (LUPI). We devised a new method to generate images
between visible and infrared domains that provide additional information to
train a deep ReID model through an intermediate domain adaptation. In
particular, by employing color-free and multi-step triplet loss objectives
during training, our method provides common feature representation spaces that
are robust to large visible-infrared domain shifts. % Experimental results on
challenging visible-infrared ReID datasets indicate that our proposed approach
consistently improves matching accuracy, without any computational overhead at
test time. The code is available at:
\href{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI}{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI
End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification
Person re-identification (re-ID) remains challenging in a real-world
scenario, as it requires a trained network to generalise to totally unseen
target data in the presence of variations across domains. Recently, generative
adversarial models have been widely adopted to enhance the diversity of
training data. These approaches, however, often fail to generalise to other
domains, as existing generative person re-identification models have a
disconnect between the generative component and the discriminative feature
learning stage. To address the on-going challenges regarding model
generalisation, we propose an end-to-end domain adaptive attention network to
jointly translate images between domains and learn discriminative re-id
features in a single framework. To address the domain gap challenge, we
introduce an attention module for image translation from source to target
domains without affecting the identity of a person. More specifically,
attention is directed to the background instead of the entire image of the
person, ensuring identifying characteristics of the subject are preserved. The
proposed joint learning network results in a significant performance
improvement over state-of-the-art methods on several benchmark datasets.Comment: submitted to IEEE Transactions on Information Forensics and Securit
Shape-centered Representation Learning for Visible-Infrared Person Re-identification
Current Visible-Infrared Person Re-Identification (VI-ReID) methods
prioritize extracting distinguishing appearance features, ignoring the natural
resistance of body shape against modality changes. Initially, we gauged the
discriminative potential of shapes by a straightforward concatenation of shape
and appearance features. However, two unresolved issues persist in the
utilization of shape features. One pertains to the dependence on auxiliary
models for shape feature extraction in the inference phase, along with the
errors in generated infrared shapes due to the intrinsic modality disparity.
The other issue involves the inadequately explored correlation between shape
and appearance features. To tackle the aforementioned challenges, we propose
the Shape-centered Representation Learning framework (ScRL), which focuses on
learning shape features and appearance features associated with shapes.
Specifically, we devise the Shape Feature Propagation (SFP), facilitating
direct extraction of shape features from original images with minimal
complexity costs during inference. To restitute inaccuracies in infrared body
shapes at the feature level, we present the Infrared Shape Restitution (ISR).
Furthermore, to acquire appearance features related to shape, we design the
Appearance Feature Enhancement (AFE), which accentuates identity-related
features while suppressing identity-unrelated features guided by shape
features. Extensive experiments are conducted to validate the effectiveness of
the proposed ScRL. Achieving remarkable results, the Rank-1 (mAP) accuracy
attains 76.1%, 71.2%, 92.4% (72.6%, 52.9%, 86.7%) on the SYSU-MM01, HITSZ-VCM,
RegDB datasets respectively, outperforming existing state-of-the-art methods