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 …

Is attention all you need in medical image analysis? A review.

G Papanastasiou, N Dikaios, J Huang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …

Attention fusion network for multi-spectral semantic segmentation

J Xu, K Lu, H Wang - Pattern Recognition Letters, 2021 - Elsevier
To improve the accuracy of multi-spectral semantic segmentation, an attention fusion
network (AFNet) based on deep learning is proposed. Different from current methods, the …

Convolution neural networks for optical coherence tomography (OCT) image classification

K Karthik, M Mahadevappa - Biomedical Signal Processing and Control, 2023 - Elsevier
Optical coherence tomography (OCT) is an imaging modality used to obtain a cross-
sectional image of the retina for retinal disease diagnosis. Modern diagnosis systems use …

Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

KC Pavithra, P Kumar, M Geetha… - Biocybernetics and …, 2023 - Elsevier
Abstract Diabetic Macular Edema (DME) is a potentially blinding consequence of Diabetic
Retinopathy (DR) as well as the leading cause of vision loss in diabetics. DME is …

Kernelized convolutional transformer network based driver behavior estimation for conflict resolution at unsignalized roundabout

O Sharma, NC Sahoo, NB Puhan - ISA transactions, 2023 - Elsevier
The modeling of driver behavior plays an essential role in developing Advanced Driver
Assistance Systems (ADAS) to support the driver in various complex driving scenarios. The …

B-scan attentive CNN for the classification of retinal optical coherence tomography volumes

V Das, E Prabhakararao, S Dandapat… - IEEE Signal …, 2020 - ieeexplore.ieee.org
Optical coherence tomography (OCT) enables 3D cross-sectional imaging of the retinal
tissues and has become an essential tool for the diagnosis of eye diseases. Clinically, the …

Computer-aided diagnosis through medical image retrieval in radiology

W Silva, T Gonçalves, K Härmä, E Schröder… - Scientific reports, 2022 - nature.com
Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and
consequently, to undesired diagnosis mistakes. Decision support systems can be used to …

Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism

Y Sun, H Zhang, X Yao - Journal of Biomedical Optics, 2020 - spiedigitallibrary.org
Significance: Automatic and accurate classification of three-dimensional (3-D) retinal optical
coherence tomography (OCT) images is essential for assisting ophthalmologist in the …

Automated classification of retinal OCT images using a deep multi-scale fusion CNN

V Das, S Dandapat, PK Bora - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
The automated analysis of optical coherence tomography (OCT) images can play a crucial
role in the diagnosis and management of retinal diseases. The wide variations of the retinal …