Browsing Nguyen Do Trung Chanh, PhD by Issue Date
Now showing items 1-11 of 11
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Diffeomorphism matching for fast unsupervised pretraining on radiographs
(2020)Unsupervised pretraining is an approach that leverages a large unlabeled data pool to learn data features. However, it requires billion-scale datasets and a month-long training time to surpass its supervised counterpart ... -
SEGTRANSVAE: Hybrid CNN-Transformer with Regularization for Medical Image Segmentation
(2022)Current research on deep learning for medical image segmentation highlights limitations in learning either global semantic information or local contextual information effectively. To address these challenges, this paper ... -
CapNext: Unifying Capsule and ResNeXt for Medical Image Segmentation
(2022)Capsule Network is a contemporary approach to image analysis that emphasizes part-whole relationships. However, its applications to segmentation tasks are limited due to training difficulties such as initialization and ... -
Improving local features with relevant spatial information by vision transformer for crowd counting
(2022)Vision Transformer (ViT) variants have demonstrated state-of-the-art performances in plenty of computer vision benchmarks, including crowd counting. Although Transformer-based models have shown breakthroughs in crowd ... -
Dual consistency assisted multi-confident learning for the hepatic vessel segmentation using noisy labels
(2022)Noisy hepatic vessel labels from Computer Tomography (CT) are popular due to vessels’ low-contrast and complex morphology. This is challenging for automatic hepatic vessel segmentation, which is essential to many hepatic ... -
Benchmarking saliency methods for chest X-ray interpretation
(2022-08)Saliency methods, which produce heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. However, rigorous investigation ... -
Adaptive Proxy Anchor Loss for Deep Metric Learning
(2022-10)Deep metric learning (or simply called metric learning) uses the deep neural network to learn the representation of images, leading to widely used in many applications, e.g. image retrieval and face recognition. In the ... -
Benchmarking saliency methods for chest X-ray interpretation
(2022-10)Saliency methods, which produce heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. However, rigorous investigation ... -
Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison
(2023)Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data ... -
Revisiting reverse distillation for anomaly detection
(2023)Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off. Memory based approaches with ... -
LOGOVIT: Local-global vision transformer for object re-identification
(2023-06)Object re-identification (ReID) is prone to errors under variations in scale, illumination, complex background, and object occlusion scenarios. To overcome these challenges, attention mechanisms are employed to concentrate ...
