Search Results for author:
Found 14 papers, 5 papers with code
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Associations of SAM's accuracy with six factors were computed, independently and jointly, including segmentation difficulties as measured by segmentation ability score and by Dice overlap in U-Net, image dimension, size of the target region, image modality, and contrast.
U-Netmer: U-Net meets Transformer for medical image segmentation
The global-context information among local patches is learnt by the self-attention mechanism in Transformer and U-Net segments each local patch instead of flattening into tokens to solve the `token-flatten" problem.
Segmentation Ability Map: Interpret deep features for medical image segmentation
In addition, our method can provide a mean SA score which can give a performance estimation of the output on the test images without ground-truth.
WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images
The WHU-Stereo dataset can serve as a challenging benchmark for stereo matching of high-resolution satellite images, and performance evaluation of deep learning models.
Deep Relation Learning for Regression and Its Application to Brain Age Estimation
In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images.
Global-Local Transformer for Brain Age Estimation
The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age.
GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification
The spatial relationship between the sequence of fragments is modeled by the recurrent neural network (RNN) to strengthen the discriminative ability of the local fragment features.
FragNet: Writer Identification using Deep Fragment Networks
Writer identification based on a small amount of text is a challenging problem.
Brain Age Estimation Using LSTM on Children's Brain MRI
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis.
6D Object Pose Estimation without PnP
On this basis, a special layer, Collinear Equation Layer, is added next to region layer to output the 2D projections of the 3D bounding boxs corners.
6D Object Pose Estimation Based on 2D Bounding Box
Our system trains a novel convolutional neural network to regress the unit quaternion, which represents the 3D rotation, from the partial image inside the bounding box returned by 2D detection systems.
DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning
This paper presents a novel iterative deep learning framework and apply it for document enhancement and binarization.
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure.
Open Set Chinese Character Recognition using Multi-typed Attributes
Recognition of Off-line Chinese characters is still a challenging problem, especially in historical documents, not only in the number of classes extremely large in comparison to contemporary image retrieval methods, but also new unseen classes can be expected under open learning conditions (even for CNN).
