Medical image segmentation with limited supervision: a review of deep network models
J Peng, Y Wang - IEEE Access, 2021 - ieeexplore.ieee.org
Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …
cutting-edge models rely heavily on large-scale annotated training examples, which are …
Weakly Supervised Deep Learning in Radiology
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based
image analysis in radiology. Traditionally, DL models have been trained with strongly …
image analysis in radiology. Traditionally, DL models have been trained with strongly …
SMU-Net: Saliency-guided morphology-aware U-Net for breast lesion segmentation in ultrasound image
Z Ning, S Zhong, Q Feng, W Chen… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Deep learning methods, especially convolutional neural networks, have been successfully
applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern …
applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern …
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 …
Automatic skull stripping of rat and mouse brain MRI data using U-Net
LM Hsu, S Wang, P Ranadive, W Ban… - Frontiers in …, 2020 - frontiersin.org
Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, aka, skull
stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly …
stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly …
Breast tumor segmentation in DCE-MRI with tumor sensitive synthesis
Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR)
images is a critical step for early detection and diagnosis of breast cancer. However …
images is a critical step for early detection and diagnosis of breast cancer. However …
Global-Local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images
Accurate and reliable detection of abnormal lymph nodes in magnetic resonance (MR)
images is very helpful for the diagnosis and treatment of numerous diseases. However, it is …
images is very helpful for the diagnosis and treatment of numerous diseases. However, it is …
Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouring
JK Udupa, T Liu, C Jin, L Zhao, D Odhner… - Medical …, 2022 - Wiley Online Library
Background Automatic segmentation of 3D objects in computed tomography (CT) is
challenging. Current methods, based mainly on artificial intelligence (AI) and end‐to‐end …
challenging. Current methods, based mainly on artificial intelligence (AI) and end‐to‐end …
RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions
W Ding, M Abdel-Basset, H Hawash - Information sciences, 2021 - Elsevier
The segmentation of COVID-19 lesions from computed tomography (CT) scans is crucial to
develop an efficient automated diagnosis system. Deep learning (DL) has shown success in …
develop an efficient automated diagnosis system. Deep learning (DL) has shown success in …
Volume-awareness and outlier-suppression co-training for weakly-supervised MRI breast mass segmentation with partial annotations
Segmenting breast mass from magnetic resonance imaging (MRI) scans is an important step
in the breast cancer diagnostic procedure for physicians and computer-aided diagnosis …
in the breast cancer diagnostic procedure for physicians and computer-aided diagnosis …