Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
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 …
Clip-driven universal model for organ segmentation and tumor detection
An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …
segmentation and tumor detection. However, due to the small size and partially labeled …
Paco: Parts and attributes of common objects
Object models are gradually progressing from predicting just category labels to providing
detailed descriptions of object instances. This motivates the need for large datasets which …
detailed descriptions of object instances. This motivates the need for large datasets which …
Learning from multiple datasets with heterogeneous and partial labels for universal lesion detection in CT
Large-scale datasets with high-quality labels are desired for training accurate deep learning
models. However, due to the annotation cost, datasets in medical imaging are often either …
models. However, due to the annotation cost, datasets in medical imaging are often either …
[HTML][HTML] 深度学习在医学影像中的应用综述
施俊， 汪琳琳， 王珊珊， 陈艳霞， 王乾， 魏冬铭， 梁淑君… - 2020 - cjig.cn
摘要深度学习能自动从大样本数据中学习获得优良的特征表达, 有效提升各种机器学习任务的
性能, 已广泛应用于信号处理, 计算机视觉和自然语言处理等诸多领域. 基于深度学习的医学影像 …
性能, 已广泛应用于信号处理, 计算机视觉和自然语言处理等诸多领域. 基于深度学习的医学影像 …
Reinventing 2d convolutions for 3d images
There have been considerable debates over 2D and 3D representation learning on 3D
medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they …
medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they …
ENet: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
Developing an effective liver and liver tumor segmentation model from CT scans is very
important for the success of liver cancer diagnosis, surgical planning and cancer treatment …
important for the success of liver cancer diagnosis, surgical planning and cancer treatment …
SATr: Slice attention with transformer for universal lesion detection
Abstract Universal Lesion Detection (ULD) in computed tomography plays an essential role
in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input …
in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input …
Deep lesion tracker: monitoring lesions in 4D longitudinal imaging studies
Monitoring treatment response in longitudinal studies plays an important role in clinical
practice. Accurately identifying lesions across serial imaging follow-up is the core to the …
practice. Accurately identifying lesions across serial imaging follow-up is the core to the …