An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets

Lee, Hyunkwang; Yune, Sehyo; Mansouri, Mohammad; Kim, Myeongchan; Tajmir, Shahein; Guerrier, Claude E; Ebert, Sarah A; Pomerantz, Stuart R.; Romero, Javier; Kamalian, Shahmir; González, Ramón; Lev, Michael H.; Do, Synho

doi:10.1038/s41551-018-0324-9

Cited by 356 publications

(271 citation statements)

References 30 publications

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“…The majority of this work has focused either on a two-class detection problem where the method detects the presence of an ICH [6][7][8][9][10][11][12][13][14][15][16][16][17][18][19] or as a multi-class classification problem, where the goal is to detect the ICH sub-types [6,8,11,15,[17][18][19]. 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…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…VGG-16 was used as the CNN model, and bidirectional Gated Recurrent Unit (GRU) was used as the RNN model. RNN layer had the same functionality of the slice interpolation technique proposed by [17], but it was more flexible with respect to the number of adjacent slices included in the classification. The algorithm was trained and validated on 2,537 CT scans and tested on 299 CT scans.…”

Section: Intracranial Hemorrhage Detectionmentioning

confidence: 99%

“…Similar to the work of Jnawalia et al [14], Lee and colleagues used transfer learning on an ensemble of four well-known CNN models to detect the ICH sub-types and bleeding points [17]. The four models were VGG-16, ResNet-50, Inception-v3, and Inception-ResNet-v2.…”

Section: Intracranial Hemorrhage Detectionmentioning

confidence: 99%

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Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Hssayeni

Croock

Salman

et al. 2020

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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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Intracranial Hemorrhage Detectionmentioning

confidence: 99%

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

148

111

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“…Since then, CNNs have matched and even surpassed human performance on natural image recognition tasks [12]. Similar developments have taken place in medicine, where CNNs matched the performance of experts in CT-screening for lung cancer [13,14], retinal assessment [3,15] and intracranial hemorrhage detection [16]. However, human performance in computer vision on medical images was only achieved but not surpassed.…”

Section: Introductionmentioning

confidence: 87%

Breaking Medical Data Sharing Boundaries by Employing Artificial Radiographs

Han

Nebelung

Haarburger

et al. 2019

Preprint

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Artificial intelligence (AI) has the potential to change medicine fundamentally. Here, expert knowledge provided by AI can enhance diagnosis by comprehensive and user independent integration of multiple image features. Unfortunately, existing algorithms often stay behind expectations, as databases used for training are usually too small, incomplete, and heterogeneous in quality. Additionally, data protection constitutes a serious obstacle to data sharing. We propose to use generative models (GM) to produce high-resolution artificial radiographs, which are free of personal identifying information. Blinded analyses by computer vision and radiology experts proved the high similarity of artificial and real radiographs. The combination of multiple GM improves the performance of computer vision algorithms and the integration of artificial data into patient data repositories can compensate for underrepresented disease entities. Furthermore, the low computational effort of our method complies with existing IT infrastructure in hospitals and thus facilitates its dissemination. We envision that our approach could lead to scalable databases of anonymous medical images enabling standardized radiomic analyses at multiple sites.

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Deep Learning Applications for Acute Stroke Management

Chavva

Crawford

Mazurek

et al. 2022

Annals of Neurology

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Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter‐clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice‐focused primer on DL. Next, we examine real‐world examples of DL applications in pixel‐wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter‐rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022;92:574–587

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An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets

Cited by 356 publications

References 30 publications

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Breaking Medical Data Sharing Boundaries by Employing Artificial Radiographs

Deep Learning Applications for Acute Stroke Management

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