CDDSA: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation [0.03%]
基于对比领域解缠和风格增强的医学图像通用分割方法
Ran Gu,Guotai Wang,Jiangshan Lu et al.
Ran Gu et al.
Generalization to previously unseen images with potential domain shifts is essential for clinically applicable medical image segmentation. Disentangling domain-specific and domain-invariant features is key for Domain Generalization (DG). Ho...
Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios [0.03%]
一种吸引人的深部形态感知主动轮廓网络用于椎体轮廓提取并扩展到异构和半监督场景中
Shen Zhao,Jinhong Wang,Xinxin Wang et al.
Shen Zhao et al.
Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is indispensable for the comprehensive diagnosis and treatment of spinal diseases. However, AVBCE is challenging due to data heterogeneity, image characterist...
PAIP 2020: Microsatellite instability prediction in colorectal cancer [0.03%]
PAIP 2020：结直肠癌微卫星不稳定性预测
Kyungmo Kim,Kyoungbun Lee,Sungduk Cho et al.
Kyungmo Kim et al.
Microsatellite instability (MSI) refers to alterations in the length of simple repetitive genomic sequences. MSI status serves as a prognostic and predictive factor in colorectal cancer. The MSI-high status is a good prognostic factor in st...
Robert Wright,Alberto Gomez,Veronika A Zimmer et al.
Robert Wright et al.
The diagnostic value of ultrasound images may be limited by the presence of artefacts, notably acoustic shadows, lack of contrast and localised signal dropout. Some of these artefacts are dependent on probe orientation and scan technique, w...
TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification [0.03%]
基于Transformer的放射治疗剂量预测：从CT图像到超像素级GCN分类指导
Zhengyang Jiao,Xingchen Peng,Yan Wang et al.
Zhengyang Jiao et al.
Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To acc...
Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining [0.03%]
无需手动勾画的基于深度学习的肺栓塞检测和分段算法研究——在肺动脉造影中的应用(CTPA)
Jiantao Pu,Naciye Sinem Gezer,Shangsi Ren et al.
Jiantao Pu et al.
We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA). This algorithm is based on deep learning but does not require manual outlines of the PE...
A generic framework for embedding human brain function with temporally correlated autoencoder [0.03%]
一种基于时间相关自动编码器嵌入人脑功能的通用框架
Lin Zhao,Zihao Wu,Haixing Dai et al.
Lin Zhao et al.
Learning an effective and compact representation of human brain function from high-dimensional fMRI data is crucial for studying the brain's functional organization. Traditional representation methods such as independent component analysis ...
Automatic labeling of Parkinson's Disease gait videos with weak supervision [0.03%]
基于弱监督的帕金森病步态视频自动标注方法
Mohsen Gholami,Rabab Ward,Ravneet Mahal et al.
Mohsen Gholami et al.
Motor dysfunction in Parkinson's Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-cons...
Unsupervised pre-training of graph transformers on patient population graphs [0.03%]
基于患者人口图的图变换器无监督预训练
Chantal Pellegrini,Nassir Navab,Anees Kazi
Chantal Pellegrini
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been fully explored for clinical data analysis. An immense amount of...
BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability [0.03%]
BayeSeg：具有可解释泛化的医学图像分割的贝叶斯建模
Shangqi Gao,Hangqi Zhou,Yibo Gao et al.
Shangqi Gao et al.
Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the b...