Search Results for author:
Found 6 papers, 3 papers with code
Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection
Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data.
CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation
Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information.
PartDiff: Image Super-resolution with Partial Diffusion Models
This observation inspired us to propose the Partial Diffusion Model (PartDiff), which diffuses the image to an intermediate latent state instead of pure random noise, where the intermediate latent state is approximated by the latent of diffusing the low-resolution image.
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI
However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices.
Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention
Expert 1) were 0. 71 (PZ) and 0. 75 (TZ).
Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer
The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0. 64, while the one built with DL-based features yielded the mean AUC of 0. 73.
