Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in develo** 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 …
Deep learning techniques for diabetic retinopathy classification: A survey
Diabetic Retinopathy (DR) is a degenerative disease that impacts the eyes and is a
consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the …
consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the …
Inf-net: Automatic covid-19 lung infection segmentation from ct images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to
face an existential health crisis. Automated detection of lung infections from computed …
face an existential health crisis. Automated detection of lung infections from computed …
Unsupervised intra-domain adaptation for semantic segmentation through self-supervision
Convolutional neural network-based approaches have achieved remarkable progress in
semantic segmentation. However, these approaches heavily rely on annotated data which …
semantic segmentation. However, these approaches heavily rely on annotated data which …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
Understanding adversarial attacks on deep learning based medical image analysis systems
Deep neural networks (DNNs) have become popular for medical image analysis tasks like
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …
Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation
In this paper, we propose a novel semi-supervised learning (SSL) framework named
BoostMIS that combines adaptive pseudo labeling and informative active annotation to …
BoostMIS that combines adaptive pseudo labeling and informative active annotation to …
CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of
permanent blindness in the working-age population. Automatic grading of DR and DME …
permanent blindness in the working-age population. Automatic grading of DR and DME …
RTNet: relation transformer network for diabetic retinopathy multi-lesion segmentation
Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting
ophthalmologists in diagnosis. Although many researches have been conducted on this …
ophthalmologists in diagnosis. Although many researches have been conducted on this …
Clip in medical imaging: A comprehensive survey
Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …
paradigm, successfully introduces text supervision to vision models. It has shown promising …