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Found 6 papers, 0 papers with code
A self-training framework for glaucoma grading in OCT B-scans
Particularly, the proposed two-step learning methodology resorts to pseudo-labels generated during the first step to augment the training dataset on the target domain, which is then used to train the final target model.
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks
Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection.
A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images
Recently, bladder cancer has been significantly increased in terms of incidence and mortality.
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net
Prostate cancer is one of the most prevalent cancers worldwide.
Analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circmpapillary OCT Images
Taking into account that glaucoma is the leading cause of blindness worldwide, we propose in this paper three different learning methodologies for glaucoma detection in order to elucidate that traditional machine-learning techniques could outperform deep-learning algorithms, especially when the image data set is small.
Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks
Nowadays, glaucoma is the leading cause of blindness worldwide.
