A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Deep learning approaches for automatic localization in medical images
Recent revolutionary advances in deep learning (DL) have fueled several breakthrough
achievements in various complicated computer vision tasks. The remarkable successes and …
achievements in various complicated computer vision tasks. The remarkable successes and …
Self-supervised learning application on COVID-19 chest X-ray image classification using masked autoencoder
The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis
facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using …
facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using …
Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning
KH Kim, HW Koo, BJ Lee, SW Yoon… - Journal of the Korean …, 2021 - Springer
Cerebral hemorrhages require rapid diagnosis and intensive treatment. This study aimed to
detect cerebral hemorrhages and their locations in images using a deep learning model …
detect cerebral hemorrhages and their locations in images using a deep learning model …
Unveiling roadway hazards: Enhancing fatal crash risk estimation through multiscale satellite imagery and self-supervised cross-matching
Traffic accidents threaten human lives and impose substantial financial burdens annually.
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …
Multi-modal data analysis for alzheimer's disease diagnosis: An ensemble model using imagery and genetic features
Alzheimer's disease (AD) is a devastating neurological disorder primarily affecting the
elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's …
elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's …
Gaze-guided class activation mapping: Leverage human visual attention for network attention in chest x-rays classification
The attention mechanism in artificial neural networks is conceptually interlinked with human
visual attention, and studies have shown that either artificial or human attention can facilitate …
visual attention, and studies have shown that either artificial or human attention can facilitate …
Contrastive cross-modal pre-training: A general strategy for small sample medical imaging
A key challenge in training neural networks for a given medical imaging task is the difficulty
of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging …
of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging …
CTG-Net: Cross-task guided network for breast ultrasound diagnosis
Deep learning techniques have achieved remarkable success in lesion segmentation and
classification between benign and malignant tumors in breast ultrasound images. However …
classification between benign and malignant tumors in breast ultrasound images. However …