MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons
Precise determination and assessment of bladder cancer (BC) extent of muscle invasion
involvement guides proper risk stratification and personalized therapy selection. In this …
involvement guides proper risk stratification and personalized therapy selection. In this …
Position-based anchor optimization for point supervised dense nuclei detection
Nuclei detection is one of the most fundamental and challenging problems in
histopathological image analysis, which can localize nuclei to provide effective computer …
histopathological image analysis, which can localize nuclei to provide effective computer …
High-level prior-based loss functions for medical image segmentation: A survey
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …
performance for supervised medical image segmentation, across various imaging modalities …
Deep learning in medical ultrasound image segmentation: a review
Z Wang - arXiv preprint arXiv:2002.07703, 2020 - arxiv.org
Applying machine learning technologies, especially deep learning, into medical image
segmentation is being widely studied because of its state-of-the-art performance and results …
segmentation is being widely studied because of its state-of-the-art performance and results …
CAT: Constrained adversarial training for anatomically-plausible semi-supervised segmentation
Deep learning models for semi-supervised medical image segmentation have achieved
unprecedented performance for a wide range of tasks. Despite their high accuracy, these …
unprecedented performance for a wide range of tasks. Despite their high accuracy, these …
Weakly supervised segmentation with cross-modality equivariant constraints
Weakly supervised learning has emerged as an appealing alternative to alleviate the need
for large labeled datasets in semantic segmentation. Most current approaches exploit class …
for large labeled datasets in semantic segmentation. Most current approaches exploit class …
Self-ensembling co-training framework for semi-supervised COVID-19 CT segmentation
The coronavirus disease 2019 (COVID-19) has become a severe worldwide health
emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed …
emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed …
[HTML][HTML] ex-vit: A novel explainable vision transformer for weakly supervised semantic segmentation
Recently vision transformer models have become prominent models for a multitude of vision
tasks. These models, however, are usually opaque with weak feature interpretability, making …
tasks. These models, however, are usually opaque with weak feature interpretability, making …
[HTML][HTML] Constrained unsupervised anomaly segmentation
Current unsupervised anomaly localization approaches rely on generative models to learn
the distribution of normal images, which is later used to identify potential anomalous regions …
the distribution of normal images, which is later used to identify potential anomalous regions …
Mixed-UNet: Refined class activation mapping for weakly-supervised semantic segmentation with multi-scale inference
Y Liu, L Lian, E Zhang, L Xu, C Xiao… - Frontiers in Computer …, 2022 - frontiersin.org
Deep learning techniques have shown great potential in medical image processing,
particularly through accurate and reliable image segmentation on magnetic resonance …
particularly through accurate and reliable image segmentation on magnetic resonance …