MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons

MG Bandyk, DR Gopireddy, C Lall, KC Balaji… - Computers in Biology …, 2021 - Elsevier
Precise determination and assessment of bladder cancer (BC) extent of muscle invasion
involvement guides proper risk stratification and personalized therapy selection. In this …

Position-based anchor optimization for point supervised dense nuclei detection

J Yao, L Han, G Guo, Z Zheng, R Cong, X Huang… - Neural Networks, 2024 - Elsevier
Nuclei detection is one of the most fundamental and challenging problems in
histopathological image analysis, which can localize nuclei to provide effective computer …

High-level prior-based loss functions for medical image segmentation: A survey

R El Jurdi, C Petitjean, P Honeine, V Cheplygina… - Computer Vision and …, 2021 - Elsevier
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
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 …

CAT: Constrained adversarial training for anatomically-plausible semi-supervised segmentation

P Wang, J Peng, M Pedersoli, Y Zhou… - … on Medical Imaging, 2023 - ieeexplore.ieee.org
Deep learning models for semi-supervised medical image segmentation have achieved
unprecedented performance for a wide range of tasks. Despite their high accuracy, these …

Weakly supervised segmentation with cross-modality equivariant constraints

G Patel, J Dolz - Medical image analysis, 2022 - Elsevier
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 …

Self-ensembling co-training framework for semi-supervised COVID-19 CT segmentation

C Li, L Dong, Q Dou, F Lin, K Zhang… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
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 …

[HTML][HTML] ex-vit: A novel explainable vision transformer for weakly supervised semantic segmentation

L Yu, W Xiang, J Fang, YPP Chen, L Chi - Pattern Recognition, 2023 - Elsevier
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 …

[HTML][HTML] Constrained unsupervised anomaly segmentation

J Silva-Rodríguez, V Naranjo, J Dolz - Medical Image Analysis, 2022 - Elsevier
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 …

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 …