Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

Arbabshirani, Mohammad R.; Fornwalt, Brandon K.; Mongelluzzo, Gino; Suever, Jonathan D.; Geise, Brandon D.; Patel, Aalpen A.; Moore, Gregory J.

doi:10.1038/s41746-017-0015-z

Cited by 351 publications

(241 citation statements)

References 31 publications

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“…Increasing the threshold to 0.8 resulted in 73.7% sensitivity, 82.4% specificity, and 82.5% accuracy, which is comparable to some methods in the literature that were trained on large datasets [13,16]. In [16], an ensemble of four 3D CNN models was trained on 10k CT scans and yielded 71.5% sensitivity and 83.5% specificity. In [13], a deep model based on DenseNet and RNN achieved 81% accuracy.…”

Section: Discussionmentioning

confidence: 55%

“…Some researchers have extended the scope and performed the ICH segmentation to identify the region of ICH [7,11,15,17,[19][20][21][22][23][24][25][26]. Most researchers validated their algorithms using small datasets [7][8][9][10][11][12][13]17,[20][21][22][24][25][26], while a few used large datasets for testing and validating [6,[14][15][16]18,19,23]. We provide a comprehensive review of the published papers for the ICH detection and segmentation ( Figure 1) in this section.…”

Section: Related Workmentioning

confidence: 99%

“…In three approaches, the CNN model was modified to process a number of CT slices at once [14][15][16]. Jnawalia and colleagues [14] proposed an ensemble of three different CNN models to perform the ICH detection.…”

Section: Intracranial Hemorrhage Detectionmentioning

confidence: 99%

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Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Hssayeni

Croock

Salman

et al. 2020

Data

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Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each slice by a consensus decision of two radiologists. Recently, fully convolutional networks (FCN) have shown to be successful in medical image segmentation. We developed a deep FCN, called U-Net, to segment the ICH regions from the CT scans in a fully automated manner. The method achieved a Dice coefficient of 0.31 for the ICH segmentation based on 5-fold cross-validation. The dataset is publicly available online at PhysioNet repository for future analysis and comparison. Dataset: https://physionet.org/content/ct-ich/1.2.0/,

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Section: Discussionmentioning

confidence: 55%

Section: Related Workmentioning

confidence: 99%

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Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Hssayeni

Croock

Salman

et al. 2020

Data

161

111

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“…ChestX-ray8 [30] was used in Li et al [57] to jointly perform classification and localization using a small number of weakly labeled examples. Patient radiology reports and other medical record data are frequently used to generate noisy labels for imaging tasks [30,[58][59][60].…”

Section: Related Workmentioning

confidence: 99%

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

Fries

Varma

Chen

et al. 2018

Preprint

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Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels.For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.May 2, 2019 2/26 apply the CNN-LSTM to a cohort of 9,230 new patients with long-term outcome and MRI data from the UK May 2, 2019 3/26Biobank. In patients identified by our classifier as having BAV, we find a 1.8-fold increase in risk of a major adverse cardiac event. These findings demonstrate how weakly supervised methods help mitigate the lack of expert-labeled training data in cardiac imaging settings, and how real-world health outcomes can be learned directly from large-scale, unlabeled medical imaging data.May 2, 2019 4/26

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“…Biomedical image analysis has been a natural area of application for deep convolutional neural networks (CNNs). Several uses of CNN-related topologies have been proposed in radiology [1,2], histopathology [3][4][5] and microscopy [6][7][8] (for a review, see [9]). High-content screening (HCS) [10][11][12][13][14][15], the use of microscopy at scale in cellular experiments, in particular, has seen progress in applying CNNbased analysis [6,7,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Training Multiscale-CNN for Large Microscopy Image Classification in One Hour

Datta

Hossain

Choi

et al. 2019

Lecture Notes in Computer Science

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Existing approaches to train neural networks that use large images require to either crop or down-sample data during pre-processing, use small batch sizes, or split the model across devices mainly due to the prohibitively limited memory capacity available on GPUs and emerging accelerators. These techniques often lead to longer time to convergence or time to train (TTT), and in some cases, lower model accuracy. CPUs, on the other hand, can leverage significant amounts of memory. While much work has been done on parallelizing neural network training on multiple CPUs, little attention has been given to tune neural network training with large images on CPUs. In this work, we train a multiscale convolutional neural network (M-CNN) to classify large biomedical images for high content screening in one hour. The ability to leverage large memory capacity on CPUs enables us to scale to larger batch sizes without having to crop or down-sample the input images. In conjunction with large batch sizes, we find a generalized methodology of linearly scaling of learning rate and train M-CNN to state-of-the-art (SOTA) accuracy of 99% within one hour. We achieve fast time to convergence using 128 two socket Intel Xeon 6148 processor nodes with 192GB DDR4 memory connected with 100Gbps Intel Omnipath architecture.

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Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

Cited by 351 publications

References 31 publications

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

Training Multiscale-CNN for Large Microscopy Image Classification in One Hour

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