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A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
Bidirectional copy-paste for semi-supervised medical image segmentation
In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …
between labeled and unlabeled data distribution. The knowledge learned from the labeled …
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
Attention mechanisms in medical image segmentation: A survey
Medical image segmentation plays an important role in computer-aided diagnosis. Attention
mechanisms that distinguish important parts from irrelevant parts have been widely used in …
mechanisms that distinguish important parts from irrelevant parts have been widely used in …
Understanding adversarial attacks on deep learning based medical image analysis systems
Deep neural networks (DNNs) have become popular for medical image analysis tasks like
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …
Multi-scale self-guided attention for medical image segmentation
Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …
segmentation, standard models still have some drawbacks. First, the use of multi-scale …
Learning calibrated medical image segmentation via multi-rater agreement modeling
In medical image analysis, it is typical to collect multiple annotations, each from a different
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …
Clustering propagation for universal medical image segmentation
Prominent solutions for medical image segmentation are typically tailored for automatic or
interactive setups posing challenges in facilitating progress achieved in one task to another …
interactive setups posing challenges in facilitating progress achieved in one task to another …
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
Whole abdominal organ segmentation is important in diagnosing abdomen lesions,
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …