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 …

Triplet attention and dual-pool contrastive learning for clinic-driven multi-label medical image classification

Y Zhang, L Luo, Q Dou, PA Heng - Medical image analysis, 2023 - Elsevier
Multi-label classification (MLC) can attach multiple labels on single image, and has
achieved promising results on medical images. But existing MLC methods still face …

FN-OCT: Disease detection algorithm for retinal optical coherence tomography based on a fusion network

Z Ai, X Huang, J Feng, H Wang, Y Tao… - Frontiers in …, 2022 - frontiersin.org
Optical coherence tomography (OCT) is a new type of tomography that has experienced
rapid development and potential in recent years. It is playing an increasingly important role …

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 …

A local and global feature disentangled network: toward classification of benign-malignant thyroid nodules from ultrasound image

SX Zhao, Y Chen, KF Yang, Y Luo… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid
cancer has increased rapidly in the past three decades and is one of the cancers with the …

A multi-modality ovarian tumor ultrasound image dataset for unsupervised cross-domain semantic segmentation

Q Zhao, S Lyu, W Bai, L Cai, B Liu, M Wu… - arXiv preprint arXiv …, 2022 - arxiv.org
Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors
in early stage with computer-aided techniques can efficiently decrease the mortality rate …

HCTNet: a hybrid ConvNet-transformer network for retinal optical coherence tomography image classification

Z Ma, Q Xie, P Xie, F Fan, X Gao, J Zhu - Biosensors, 2022 - mdpi.com
Automatic and accurate optical coherence tomography (OCT) image classification is of great
significance to computer-assisted diagnosis of retinal disease. In this study, we propose a …

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis

Y Wang, L Zhen, TE Tan, H Fu, Y Feng… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two
of the most widely used modalities in the clinical diagnosis and management of retinal …

Intraoperative glioma grading using neural architecture search and multi-modal imaging

A Xiao, B Shen, X Shi, Z Zhang, Z Zhang… - … on Medical Imaging, 2022 - ieeexplore.ieee.org
Glioma grading during surgery can help clinical treatment planning and prognosis, but
intraoperative pathological examination of frozen sections is limited by the long processing …

Gradient modulated contrastive distillation of low-rank multi-modal knowledge for disease diagnosis

X Xing, Z Chen, Y Hou, Y Yuan - Medical Image Analysis, 2023 - Elsevier
The fusion of multi-modal data, eg, medical images and genomic profiles, can provide
complementary information and further benefit disease diagnosis. However, multi-modal …