27 citations · 57 across the 8 of their papers we have counts for
8 papers
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Meng Wang, Tian Lin, Lianyu Wang +21
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classific…
Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm
Jiahong Wei, Zhun Fan
Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation. However, these CNNs rem…
Low Tensor Train- and Low Multilinear Rank Approximations for De-speckling and Compression of 3D Optical Coherence Tomography Images
Ivica Kopriva, Fei Shi, Mingying Lai +3
This paper proposes low tensor-train (TT) rank and low multilinear (ML) rank approximations for de-speckling and compression of 3D optical coherence tomography (OCT) images for a g…
Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network
Zhun Fan, Jiajie Mo, Benzhang Qiu +6
Retinal vessel segmentation is a crucial step in diagnosing and screening various diseases, including diabetes, ophthalmologic diseases, and cardiovascular diseases. In this paper,…
Non-rigid image registration using spatially region-weighted correlation ratio and GPU-acceleration
Lun Gong, Cheng Zhang, Luwen Duan +4
Objective: Non-rigid image registration with high accuracy and efficiency is still a challenging task for medical image analysis. In this work, we present the spatially region-weig…
A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus images
Zhun Fan, Jiewei Lu, Wenji Li +4
In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and e…
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
Naji Khosravan, Haydar Celik, Baris Turkbey +9
This study introduced a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists' reading experience with an automatic image…
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images
Ulas Bagci, Jayaram K. Udupa, Xinjian Chen
This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searc…