Advancements in Automatic Kidney Segmentation using Deep Learning Frameworks and Volumetric Segmentation Techniques for CT Imaging: A Review
Abstract The efficiency of Three-Dimensional Convolutional Neural Networks (3-D-CNNs) in
precisely delineating the complex architecture of the kidney has been well-established …
precisely delineating the complex architecture of the kidney has been well-established …
Medical image segmentation with domain adaptation: a survey
Deep learning (DL) has shown remarkable success in various medical imaging data
analysis applications. However, it remains challenging for DL models to achieve good …
analysis applications. However, it remains challenging for DL models to achieve good …
CANARY: An Adversarial Robustness Evaluation Platform for Deep Learning Models on Image Classification
The vulnerability of deep-learning-based image classification models to erroneous
conclusions in the presence of small perturbations crafted by attackers has prompted …
conclusions in the presence of small perturbations crafted by attackers has prompted …
A 3D Anatomy-Guided Self-Training Segmentation Framework for Unpaired Cross-Modality Medical Image Segmentation
Y Zhuang, H Liu, E Song, X Xu, Y Liao… - … on Radiation and …, 2023 - ieeexplore.ieee.org
Unsupervised domain adaptation (UDA) methods have achieved promising performance in
alleviating the domain shift between different imaging modalities. In this article, we propose …
alleviating the domain shift between different imaging modalities. In this article, we propose …
False negative/positive control for sam on noisy medical images
The Segment Anything Model (SAM) is a recently developed all-range foundation model for
image segmentation. It can use sparse manual prompts such as bounding boxes to …
image segmentation. It can use sparse manual prompts such as bounding boxes to …
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
Y Zhang, Y Wang, Z Fang, H Bian, L Cai… - arXiv preprint arXiv …, 2024 - arxiv.org
Weakly supervised segmentation methods have gained significant attention due to their
ability to reduce the reliance on costly pixel-level annotations during model training …
ability to reduce the reliance on costly pixel-level annotations during model training …
Dimix: Disentangle-and-mix based domain generalizable medical image segmentation
The rapid advancements in deep learning have revolutionized multiple domains, yet the
significant challenge lies in effectively applying this technology to novel and unfamiliar …
significant challenge lies in effectively applying this technology to novel and unfamiliar …
IRLSG: Invariant Representation Learning for Single-Domain Generalization in Medical Image Segmentation
Single-domain generalization (SDG) can efficiently enhance model generalization while
avoiding high annotation costs and privacy concerns. However, existing SDG methods are …
avoiding high annotation costs and privacy concerns. However, existing SDG methods are …
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images
Background Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation
between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for …
between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for …