Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report generation [0.03%]
像放射科医生一样推理：用于可验证报告生成的链式思维和强化学习
Peiyuan Jing,Kinhei Lee,Zhenxuan Zhang et al.
Peiyuan Jing et al.
Radiology report generation is critical for efficiency, but current models often lack the structured reasoning of experts and the ability to explicitly ground findings in anatomical evidence, which limits clinical trust and explainability. ...
Segmentation of the right ventricular myocardial infarction in multi-centre cardiac magnetic resonance images [0.03%]
多中心心脏磁共振图像中的右心室心肌梗死分割
Chao Xu,Dongaolei An,Chaolu Feng et al.
Chao Xu et al.
Right ventricular myocardial infarction (RVMI) is associated with higher in-hospital morbidity and mortality. Cardiac magnetic resonance (CMR) imaging provides crucial pathological information for diagnosis and/or treatment of RVMI. Segment...
BIASNet: A bidirectional feature alignment and semantics-guided network for weakly-supervised medical image registration [0.03%]
一种双向特征对齐和语义引导的弱监督医学图象配准网络 Biasnet
Housheng Xie,Xiaoru Gao,Guoyan Zheng
Housheng Xie
Medical image registration, which establishes spatial correspondences between different medical images, serves as a fundamental process in numerous clinical applications and diagnostic workflows. Despite significant advancement in unsupervi...
A Multi-instance Learning Network with Prototype-instance Adversarial Contrastive for Cervix Pathology Grading [0.03%]
基于原型实例对抗对比的多示例学习宫颈病理分级网络
Mingrui Ma,Furong Luo,Binlin Ma et al.
Mingrui Ma et al.
The pathological grading of cervical squamous cell carcinoma (CSCC) is a fundamental and important index in tumor diagnosis. Pathologists tend to focus on single differentiation areas during the grading process. Existing multi-instance lear...
SAM-Swin: SAM-driven dual-swin transformers with adaptive lesion enhancement for Laryngo-Pharyngeal tumor detection [0.03%]
基于自适应病灶增强的SAM驱动双Swin Transformer喉咽肿瘤检测模型
Jia Wei,Yun Li,Xiaomao Fan et al.
Jia Wei et al.
Laryngo-pharyngeal cancer (LPC) is a highly lethal malignancy in the head and neck region. Recent advancements in tumor detection, particularly through dual-branch network architectures, have significantly improved diagnostic accuracy by in...
CIA-net: Cross-modality interaction and aggregation network for ovarian tumor segmentation from multi-modal MRI [0.03%]
一种用于多模态MRI的卵巢肿瘤分割的交叉模态交互和聚合网络(CIA-net)
Yifan Gao,Yong&#x;ai Li,Xin Gao
Yifan Gao
Magnetic resonance imaging (MRI) is an essential examination for ovarian cancer, in which ovarian tumor segmentation is crucial for personalized diagnosis and treatment planning. However, ovarian tumors often present with mixed cystic and s...
MIRAGE: Medical image-text pre-training for robustness against noisy environments [0.03%]
MIRAGE：鲁棒的噪声环境下的医学图像文本预训练模型
Pujin Cheng,Yijin Huang,Li Lin et al.
Pujin Cheng et al.
Contrastive vision-language pre-training models have achieved significant success on large-scale general multi-modality datasets. However, in the medical domain, the high costs of data collection and expert annotation are likely to result i...
GEM-pRF: GPU-empowered mapping of population receptive fields for large-scale fMRI analysis [0.03%]
基于GPU的大规模fMRI人群感受野映射(GEM-pRF)技术
Siddharth Mittal,Michael Woletz,David Linhardt et al.
Siddharth Mittal et al.
Population receptive field (pRF) mapping is a fundamental technique for understanding retinotopic organisation of the human visual system. Since its introduction in 2008, however, its scalability has been severely hindered by the computatio...
Immunocto: A massive immune cell database auto-generated for histopathology [0.03%]
Immunocto：一种自动化的巨量免疫细胞病理学数据库
Mikaë El Simard,Zhuoyan Shen,Konstantin Bräutigam et al.
Mikaë El Simard et al.
With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment (TIME) is crucial to inform on prognosis and understand potential response to therapeutic agents. A key approach to charac...
Xiao Ma,Yuhui Tao,Zetian Zhang et al.
Xiao Ma et al.
Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. ...