A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation
H Jiang, Z Diao, T Shi, Y Zhou, F Wang, W Hu… - Computers in Biology …, 2023 - Elsevier
Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …
processing with the advancement of deep learning in natural image classification, detection …
Deep learning for medical image-based cancer diagnosis
Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …
diagnosis. To help readers better understand the current research status and ideas, this …
Haar wavelet downsampling: A simple but effective downsampling module for semantic segmentation
Downsampling operations such as max pooling or strided convolution are ubiquitously
utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge …
utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge …
Enhanced damage segmentation in RC components using pyramid Haar wavelet downsampling and attention U-net
W Wang, L Li, Z Qu, X Yang - Automation in Construction, 2024 - Elsevier
Damage identification in post-earthquake reinforced concrete (RC) structures based on
semantic segmentation has been recognized as a promising approach for rapid and non …
semantic segmentation has been recognized as a promising approach for rapid and non …
C2BA-UNet: A context-coordination multi-atlas boundary-aware UNet-like method for PET/CT images based tumor segmentation
S Luo, H Jiang, M Wang - Computerized Medical Imaging and Graphics, 2023 - Elsevier
Tumor segmentation is a necessary step in clinical processing that can help doctors
diagnose tumors and plan surgical treatments. Since tumors are usually small, the locations …
diagnose tumors and plan surgical treatments. Since tumors are usually small, the locations …
Deep learning for automatic tumor lesions delineation and prognostic assessment in multi-modality pet/ct: A prospective survey
Tumor lesion segmentation and staging in cancer patients are one of the most challenging
tasks for radiologists to recommend better treatment planning like radiation therapy …
tasks for radiologists to recommend better treatment planning like radiation therapy …
Evaluation of mediastinal lymph node segmentation of heterogeneous CT data with full and weak supervision
Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic
management, and assessing response to therapy. Current standard practice for quantifying …
management, and assessing response to therapy. Current standard practice for quantifying …
Structural tensor and frequency guided semi‐supervised segmentation for medical images
X Leng, X Wang, W Yue, J **, G Xu - Medical Physics, 2024 - Wiley Online Library
Background The method of semi‐supervised semantic segmentation entails training with a
limited number of labeled samples alongside many unlabeled samples, aiming to reduce …
limited number of labeled samples alongside many unlabeled samples, aiming to reduce …
[HTML][HTML] Multi-modal tumor segmentation methods based on deep learning: a narrative review
Methods In in the PubMed and Google Scholar databases, the keywords “multi-
modal”,“deep learning”, and “tumor segmentation” were used to systematically search …
modal”,“deep learning”, and “tumor segmentation” were used to systematically search …
Joint lymphoma lesion segmentation and prognosis prediction from baseline FDG-PET images via multitask convolutional neural networks
Objective: Lymphoma lesion segmentation and prognosis prediction from baseline FDG-
PET images are valuable for tailoring and adapting a treatment plan for patients with Diffuse …
PET images are valuable for tailoring and adapting a treatment plan for patients with Diffuse …