PIVOTS: Aligning unseen structures using preoperative to intraoperative volume-to-surface registration for liver navigation [0.03%]
基于术前到术中体积对齐表面的配准用于肝脏导航以校正未见结构偏移的轴标点方法
Peng Liu,Bianca Güttner,Yutong Su et al.
Peng Liu et al.
Non-rigid registration is essential for augmented reality-guided laparoscopic liver surgery, as it enables the fusion of preoperative information such as tumor location and vascular structures into the limited intraoperative view, thereby e...
Two-stage robust 3D CTA-2D DSA alignment via vascular-aware rigid and pyramid-based hierarchical non-rigid registration [0.03%]
基于血管感知的刚体和金字塔分层非刚体配准的两阶段鲁棒3D CTA-2D DSA对齐方法
Xiaosong Xiong,Caiwen Jiang,Han Wu et al.
Xiaosong Xiong et al.
Accurate vascular structural alignment between 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) can significantly enhance visualization during percutaneous coronary intervention (PCI), thereby imp...
A false discovery rate control method using a fully connected hidden Markov random field for neuroimaging data [0.03%]
一种用于神经影像数据的全连接隐马尔可夫随机场假阳性率控制方法
Taehyo Kim,Qiran Jia,Mony J de Leon et al.
Taehyo Kim et al.
False discovery rate (FDR) control methods are essential for voxel-wise multiple testing in neuroimaging data analysis, where hundreds of thousands or even millions of tests are conducted to detect brain regions associated with disease-rela...
A spatiotemporal dependency-aware lightweight CNN-ViT network for 3D MRF with a balanced acceleration strategy [0.03%]
一种时空依赖感知轻量级CNN-VIT网络的3D MRF平衡加速策略
Jintao Wei,Huihui Ye,Bingchen Shao et al.
Jintao Wei et al.
The push for rapid MRI acquisition aims to enhance clinical efficiency and diagnostic consistency by shortening scan times. 3D Magnetic Resonance Fingerprinting (MRF) has emerged as a promising technique for fast, multi-parametric quantitat...
Onur Çakı,Sinan Unver,Ayse Humeyra Dur Karasayar et al.
Onur Çakı et al.
Automatic cell detection is a key task in digital pathology, where manual counting remains impractical due to its time-consuming nature and susceptibility to variability and error. Current deep learning approaches still have difficulty achi...
Towards a universal JPEG lossless recompression foundation model for pathology images: A transformer context modeling approach [0.03%]
面向病理图像的通用JPEG无损再压缩基础模型：一种变压器上下文建模方法
Tao Song,Rong Tao,Chunyan Wu et al.
Tao Song et al.
Lossless recompression of JPEG images remains fundamentally constrained by the limited modeling capacity of traditional context-mixing entropy estimators, yielding suboptimal compression ratios. Recently, CNN-based learned recompression met...
ViGNet: A clinical data-supported deep learning approach for NSCLC immunotherapy response prediction in digital pathology [0.03%]
基于临床数据的深度学习方法用于预测数字病理学中非小细胞肺癌免疫治疗反应(ViGNet)
Luoyi Kong,Shaowei Wu,Canjia Cai et al.
Luoyi Kong et al.
Histopathology is the cornerstone of oncology diagnosis, while whole-slide images (WSIs) enable the transition to digital, quantitative pathology. Leveraging WSIs to accurately predict therapeutic response is increasingly vital for advancin...
Beyond attention heatmaps: How to get better explanations for multiple instance learning models in histopathology [0.03%]
超越注意力热图：如何获得更好的解释以改进组实例学习模型在组织病理学中的应用
Mina Jamshidi Idaji,Julius Hense,Tom Neuhäuser et al.
Mina Jamshidi Idaji et al.
Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated into slide-level predictions. Heatmaps are widely used to va...
Diffusion-based cross-staining feature transformation for whole slide image analysis: From H&E to IHC representation learning [0.03%]
基于扩散的跨染色特征转换用于全片扫描图像分析：从H&E到IHC表示学习
Jialong Zhong,Miao Zhang,Leiye Liu et al.
Jialong Zhong et al.
In computational pathology, Hematoxylin and Eosin (H&E) staining offers a cost-effective solution for tissue analysis, while Immunohistochemistry (IHC) delivers specific biomarker expression at substantially higher cost and operational comp...
From pixels to polygons: A survey of deep learning approaches for medical image-to-mesh reconstruction [0.03%]
从像素到多边形：医学图像到网格重建的深度学习方法综述
Fengming Lin,Arezoo Zakeri,Yidan Xue et al.
Fengming Lin et al.
Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimensional mesh models that are critical in computational medicine and in silico trials for advanc...