Search Results for author:
Found 11 papers, 3 papers with code
DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation
Specifically, a novel message passing manner based on anisotropic diffusion is developed to update the node features, which can distinguish the isomorphic graphs and improve the expressivity of graph nodes for complex samples.
Multilevel Perception Boundary-guided Network for Breast Lesion Segmentation in Ultrasound Images
Moreover, to improve the segmentation performance for tumor boundaries, a multi-level boundary-enhanced segmentation (BS) loss is proposed.
Progressive Dual Priori Network for Generalized Breast Tumor Segmentation
To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast amd irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different sites.
AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention
Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion.
Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset
The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22. 96, \chi^2=105. 04) and (OR=7. 06, \chi^2=290. 11).
Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background.
Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications
Since path optimization is the core of any search algorithms, including A*, the 4D cylindrical grid provides for a search space that can embed further knowledge in form of cell properties, including the presence of obstacles and volumetric occupancy of the entire industrial robot body for obstacle avoidance applications.
Scalar Coupling Constant Prediction Using Graph Embedding Local Attention Encoder
The experimental results demonstrate that, compared to the traditional chemical bond structure representations, the rotation and translation invariant structure representations proposed in this work can improve the SCC prediction accuracy; with the graph embedded local self-attention, the mean absolute error (MAE) of the prediction model in the validation set decreases from 0. 1603 Hz to 0. 1067 Hz; using the classification based loss function instead of the scaled regression loss, the MAE of the predicted SCC can be decreased to 0. 0963 HZ, which is close to the quantum chemistry standard on CHAMPS dataset.
CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging
The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing.
Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics
Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making.
Convolutional Restricted Boltzmann Machine Based-Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
We proposed a novel convolutional restricted Boltzmann machine CRBM-based radiomic method for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy treatment (NACT) in breast cancer.
