E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image [0.03%]
基于全切片图像的端到端弱监督深度卷积网络肺癌分类方法E2EFP-MIL
Lei Cao,Jie Wang,Yuanyuan Zhang et al.
Lei Cao et al.
Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on m...
DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT [0.03%]
杜SFE：一种用于心脏SPECT和CT跨模式配准的双通道挤压融合激励共注意力模型
Xiongchao Chen,Bo Zhou,Huidong Xie et al.
Xiongchao Chen et al.
Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Attenuation maps (μ-maps) derived from computed tomography (CT) are utilized for at...
Learning what and where to segment: A new perspective on medical image few-shot segmentation [0.03%]
从零开始：医学图像Few-Shot分割的新视角
Yong Feng,Yonghuai Wang,Honghe Li et al.
Yong Feng et al.
Traditional medical image segmentation methods based on deep learning require experts to provide extensive manual delineations for model training. Few-shot learning aims to reduce the dependence on the scale of training data but usually sho...
From modern CNNs to vision transformers: Assessing the performance, robustness, and classification strategies of deep learning models in histopathology [0.03%]
从现代CNN到视觉变压器：评估深度学习模型在组织病理学中的性能、鲁棒性和分类策略
Maximilian Springenberg,Annika Frommholz,Markus Wenzel et al.
Maximilian Springenberg et al.
While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accu...
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion [0.03%]
基于深度多元图像补全的高分辨率乳腺图像无监督异常定位
Nicholas Konz,Haoyu Dong,Maciej A Mazurowski
Nicholas Konz
Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of normal images for th...
Virtual high-resolution MR angiography from non-angiographic multi-contrast MRIs: synthetic vascular model populations for in-silico trials [0.03%]
基于非血管MRI的多对比度合成血管模型人群的虚拟高分辨率MR血管成像：用于计算机试验
Yan Xia,Nishant Ravikumar,Toni Lassila et al.
Yan Xia et al.
Despite success on multi-contrast MR image synthesis, generating specific modalities remains challenging. Those include Magnetic Resonance Angiography (MRA) that highlights details of vascular anatomy using specialised imaging sequences for...
Contour-aware network with class-wise convolutions for 3D abdominal multi-organ segmentation [0.03%]
基于轮廓的类卷积网络在三维腹部多器官分割中的应用研究
Hongjian Gao,Mengyao Lyu,Xinyue Zhao et al.
Hongjian Gao et al.
Accurate delineation of multiple organs is a critical process for various medical procedures, which could be operator-dependent and time-consuming. Existing organ segmentation methods, which were mainly inspired by natural image analysis te...
Learning pyramidal multi-scale harmonic wavelets for identifying the neuropathology propagation patterns of Alzheimer's disease [0.03%]
学习金字塔多尺度谐波小波以识别阿尔茨海默病神经病理传播模式
Huan Liu,Hongmin Cai,Defu Yang et al.
Huan Liu et al.
Previous studies have established that neurodegenerative disease such as Alzheimer's disease (AD) is a disconnection syndrome, where the neuropathological burdens often propagate across the brain network to interfere with the structural and...
A reinforcement learning approach for VQA validation: An application to diabetic macular edema grading [0.03%]
用于VQA验证的强化学习方法：糖尿病黄斑水肿分级中的应用
Tatiana Fountoukidou,Raphael Sznitman
Tatiana Fountoukidou
Recent advances in machine learning models have greatly increased the performance of automated methods in medical image analysis. However, the internal functioning of such models is largely hidden, which hinders their integration in clinica...
Steffen Czolbe,Paraskevas Pegios,Oswin Krause et al.
Steffen Czolbe et al.
Image registration aims to find geometric transformations that align images. Most algorithmic and deep learning-based methods solve the registration problem by minimizing a loss function, consisting of a similarity metric comparing the alig...