Search Results for author:
Found 7 papers, 3 papers with code
FGR-Net:Interpretable fundus imagegradeability classification based on deepreconstruction learning
The extracted features by the autoencoder are then fed into a deep classifier network to distinguish between gradable and ungradable fundus images.
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly KITTI and Make3D datasets along with a reduction of 40% in the number of trainable parameters compared to the state of the art solutions.
Ranked #2 on
Monocular Depth Estimation
on KITTI
Absolute distance prediction based on deep learning object detection and monocular depth estimation models
Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras.
REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network.
Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition. Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE.
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography.
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge.
