Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network
Training robust deep learning (DL) systems for medical image classification or segmentation
is challenging due to limited images covering different disease types and severity. We …
is challenging due to limited images covering different disease types and severity. We …
Imaging in inflammatory bowel disease: Current and future perspectives
N Shaban, CL Hoad, I Naim, M Alshammari… - Frontline …, 2022 - fg.bmj.com
The use of cross-sectional imaging and ultrasonography has long complemented
endoscopic assessment of inflammatory bowel disease (IBD). Clinical symptoms alone are …
endoscopic assessment of inflammatory bowel disease (IBD). Clinical symptoms alone are …
A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling
Robust organ segmentation is a prerequisite for computer-aided diagnosis, quantitative
imaging analysis, pathology detection, and surgical assistance. For organs with high …
imaging analysis, pathology detection, and surgical assistance. For organs with high …
Interpretability-driven sample selection using self supervised learning for disease classification and segmentation
In supervised learning for medical image analysis, sample selection methodologies are
fundamental to attain optimum system performance promptly and with minimal expert …
fundamental to attain optimum system performance promptly and with minimal expert …
Multi-scale context-guided deep network for automated lesion segmentation with endoscopy images of gastrointestinal tract
Accurate lesion segmentation based on endoscopy images is a fundamental task for the
automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually …
automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually …
Structure preserving stain normalization of histopathology images using self supervised semantic guidance
Although generative adversarial network (GAN) based style transfer is state of the art in
histopathology color-stain normalization, they do not explicitly integrate structural …
histopathology color-stain normalization, they do not explicitly integrate structural …
Lung lesion extraction using a toboggan based growing automatic segmentation approach
The accurate segmentation of lung lesions from computed tomography (CT) scans is
important for lung cancer research and can offer valuable information for clinical diagnosis …
important for lung cancer research and can offer valuable information for clinical diagnosis …
3-D RoI-aware U-net for accurate and efficient colorectal tumor segmentation
Segmentation of colorectal cancerous regions from 3-D magnetic resonance (MR) images is
a crucial procedure for radiotherapy. Automatic delineation from 3-D whole volumes is in …
a crucial procedure for radiotherapy. Automatic delineation from 3-D whole volumes is in …
Prostate MRI segmentation using learned semantic knowledge and graph cuts
D Mahapatra, JM Buhmann - IEEE Transactions on Biomedical …, 2013 - ieeexplore.ieee.org
We propose a fully automated method for prostate segmentation using random forests (RFs)
and graph cuts. A volume of interest (VOI) is automatically selected using supervoxel …
and graph cuts. A volume of interest (VOI) is automatically selected using supervoxel …
Unsupervised domain adaptation using feature disentanglement and GCNs for medical image classification
The success of deep learning has set new benchmarks for many medical image analysis
tasks. However, deep models often fail to generalize in the presence of distribution shifts …
tasks. However, deep models often fail to generalize in the presence of distribution shifts …