Medical Image Computing and Computer-Assisted Intervention

By Guang-Zhong Yang, David J. Hawkes, Daniel Rueckert, J. Noble, Christopher J. Taylor. 2009

1,290 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/337f6e56f11f7deb7c25408c29904280d85404cd

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Citations in the corpus

Machine Learning and Radiology (2012, background): “In the work of Papademetris et al., they combine point-feature and intensity information together for non-rigid registration (Papademetris et al., 2004).”
AUC Maximization in the Era of Big Data and AI: A Survey (2022, methodology): “They use the PathCamelyon dataset for this task, which consists of 294,912 color images (96×96) extracted from histopathologic scans of lymph node section for training and 32,768 images for testing with balanced class ratio [8, 160].”
Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions (2016, background): “Multiple studies have shown that skill metrics can be derived using statistical analysis (HMM [28,53,54]) of instrument motion from this data [55].”
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze (2010, background): “Colors have mostly been employed for skin-color modeling [5], [38], [162], [132], [133], [76], [75], [78], [77], but there are also some attempts to model the color distribution of eye regions [46], [48].”
Deformable medical image registration: setting the state of the art with discrete methods. (2011, background): “Multisubject non-rigid registration of brain MRI using intensity and geometric features.”
The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset (2020, methodology): “The batch sizes used in this experiment were B = [16 , 32 , 64 , 128 , 256]; two optimizers were used, namely SGD and Adam optimizers, and two learning rates were used for each optimizer of 0.001 and 0.0001.”
Simulation of hyperelastic materials in real-time using Deep Learning (2019, unknown): “This phase ”corresponds” to the training of the U-Net and is generally faster.”
Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data (2019, background): “While deep CNNs generally ben-129 eﬁt from predicting multiple correlated output variables, more complex networks require increased computation and training iterations, or epochs (for a review of multi-task learn-131 ing, see Ruder, 2017).”
Reconstruction of fetal brain MRI with intensity matching and complete outlier removal (2012, methodology): “In their work, Rousseau et al. (2010) established that under ideal conditions, three orthogonal stacks of thick slices with no gaps are sufficient if super-resolution reconstruction is combined with edge-preserving regularisation.”
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, unknown): “In the context of medical image analysis, attention models have been exploited for medical report generation (Zhang et al., 2017a,b) as well as joint image and text classification (Wang et al., 2018).”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, unknown): “Additionally, these networks often outperform traditional approaches with respect to accuracy and have become increasingly popular for pixel or voxel-wise semantic segmentation tasks in computer vision and biomedical imaging (Long et al., 2015; Ronneberger et al., 2015; Noh et al., 2015; Badrinaraya”
Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates (2014, unknown): “…have demonstrated sufficient flexibility to accommodate atlases that are somehow exceptional such as those derived from serial histological data (Chakravarty et al., 2006; Yelnik et al., 2007) or highresolution MRI data that enables robust identification of hippocampal subfields (Mueller and…”
Transforms and Operators for Directional Bioimage Analysis: A Survey. (2016, methodology): “A sample application of the Hessian filter is vessel enhancement (Frangi et al. 1998).”
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study (2010, background): “A large number of systems for nodule detection have been proposed in the literature (Li et al., 2008; Arimura et al., 2004; Armato et al., 2001, 2002; Bae et al., 2005; Bellotti et al., 2007; Brown et al., 2003; Dehmeshki et al., 2007; Enquobahrie et al., 2007; Farag et al., 2004; Ge et al., 2005; K”
Dense image registration through MRFs and efficient linear programming (2008, unknown): “The medical application in the second segmentation task is similar to the one described in [9].”
Generative Adversarial Network in Medical Imaging: A Review (2018, background): “A similar idea was explored by Liao et al. (2018) in sparse view CT reconstruction.”