Jonas M Van Elburg,Natalia V Korobova,Mohammad M Islam et al.
Jonas M Van Elburg et al.
Dynamic contrast-enhanced (DCE) MRI is a powerful technique for detecting and characterising various diseases by quantifying tissue perfusion. However, accurate perfusion quantification remains challenging due to noisy data and the complexi...
PISCO: Self-supervised k-space regularization for improved neural implicit k-space representations of dynamic MRI [0.03%]
基于自监督k空间正则化的改进动态MRI神经隐式k空间表示方法
Veronika Spieker,Hannah Eichhorn,Wenqi Huang et al.
Veronika Spieker et al.
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time, and thereby available training data, results in severe per...
Perivascular space identification nnUNet for generalised usage (PINGU) [0.03%]
用于通用性的血管周围空间识别nnUNet(PINGU)
Benjamin Sinclair,William Pham,Lucy Vivash et al.
Benjamin Sinclair et al.
Perivascular spaces (PVSs) form a central component of the brain's waste clearance system, the glymphatic system. These structures are visible on MRIs when enlarged, and their morphology is associated with aging and neurological disease. Ma...
D-EDL: Differential evidential deep learning for robust medical out-of-distribution detection [0.03%]
差异证据深度学习在鲁棒医学异常检测中的应用
Wei Fu,Yufei Chen,Yuqi Liu et al.
Wei Fu et al.
In computer-aided diagnosis, the extreme imbalance in disease incidence rates often results in the omission of rare conditions, leading to out-of-distribution (OOD) samples during testing. To prevent unreliable diagnostic outputs, detecting...
Spatial transcriptomics expression prediction from histopathology based on cross-modal mask reconstruction and contrastive learning [0.03%]
基于跨模态掩码重建和对比学习的病理组织学空间转录组表达预测
Junzhuo Liu,Markus Eckstein,Zhixiang Wang et al.
Junzhuo Liu et al.
Spatial transcriptomics is a technology that captures gene expression at different spatial locations, widely used in tumor microenvironment analysis and molecular profiling of histopathology, providing valuable insights into resolving gene ...
Robust simultaneous multislice MRI reconstruction using slice-wise learned generative diffusion priors [0.03%]
基于片层学习生成扩散先验的鲁棒多重切片MRI重建方法
Shoujin Huang,Guanxiong Luo,Yunlin Zhao et al.
Shoujin Huang et al.
Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to complex signal interactions between and within the excited ...
Ark+: Supervised training a single high-performance AI foundation model from many differently labeled datasets-no label consolidation required [0.03%]
ARK+: 在无需标签整合的情况下，利用多个不同标注的数据集监督训练单一高性能AI基础模型
DongAo Ma,Jiaxuan Pang,Shivasakthi Senthil Velan et al.
DongAo Ma et al.
This article presents a methodological breakthrough in supervised learning for training a single, robust, and high-performance artificial intelligence (AI) model using a multitude of datasets labeled differently-yet requiring no manual labe...
PL-Seg: Partially labeled abdominal organ segmentation via classwise orthogonal contrastive learning and progressive self-distillation [0.03%]
基于类正交对比学习和渐进式自我蒸馏的腹部器官部分标注分割方法
He Li,Xiangde Luo,Jia Fu et al.
He Li et al.
Accurate segmentation of abdominal organs in Computed Tomography (CT) scans is crucial for effective lesion diagnosis, radiotherapy planning, and patient follow-up. Although deep learning has shown great performance with fully supervised le...
Unsupervised learning of spatially varying regularization for diffeomorphic image registration [0.03%]
无监督学习空间变化正则化以求得互信息下的微分同胚图像配准
Junyu Chen,Shuwen Wei,Yihao Liu et al.
Junyu Chen et al.
Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration. Historically, optimization-based registration models have harnessed spatia...
Dejan Štepec,Maja Jerše,Snežana Đokić et al.
Dejan Štepec et al.
We present Patherea, a unified framework for point-based cell detection and classification in histopathology. Our method directly predicts cell locations and classes without intermediate representations and incorporates a hybrid Hungarian m...