Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning

Kim, Kwang Hyeon; Koo, Hae-Won; Lee, Byung-Jou; Yoon, Sangwon; Sohn, Moon‐Jun

doi:10.1007/s40042-021-00202-2

Cited by 25 publications

(8 citation statements)

References 34 publications

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“…Compared with the RSNA and CQ500 datasets, which contain hundreds of 1000s of CT scans, private or internal datasets were used in other studies on brain hematoma classification, [ 10 – 12 , 21 – 23 ] and most of these datasets were relatively small (150–2000 scans). The models used in these studies were trained with sophisticated ML pipelines, but there may have been limitations in the reproducibility and extendibility of the models, considering the wide variety of TBI abnormalities.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies using the CQ500 proposed different types of ML models to classify TBI abnormalities, such as hemorrhages observed in various parts of a brain, fractures, or midline shift. [17][18][19][20] Compared with the RSNA and CQ500 datasets, which contain hundreds of 1000s of CT scans, private or internal datasets were used in other studies on brain hematoma classification, [10][11][12][21][22][23] and most of these datasets were relatively small (150-2000 scans). The models used in these studies were trained with sophisticated ML pipelines, but there may have been limitations in the reproducibility and extendibility of the models, considering the wide variety of TBI abnormalities.…”

Section: Classification Of Ich Typesmentioning

confidence: 99%

“…We noted that no ML approaches existed that identified cerebellar tonsillar herniation. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] •Multiple TBI types [24,25] •Multiple hematoma types [10,26,27] •Multiple hematoma types [28][29][30][31] Binary-class…”

Section: Measurement Of Midline Shiftmentioning

confidence: 99%

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Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

et al. 2022

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Background: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). Methods: Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. Results: A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. Conclusion: We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.

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Section: Resultsmentioning

confidence: 99%

Section: Classification Of Ich Typesmentioning

confidence: 99%

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Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

et al. 2022

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“…In a manner akin to the COVID-19 investigations, prior anatomical segmentation was employed to categorize and illustrate mortality risks based on cardiac PET [43], Alzheimer's disease [44], and schizophrenia based on MRI [45]. Grad-CAM's low-dimensional attribution maps, however, continue to cause poor specificity even while data handling reduces the prevalence of non-target-specific characteristics [46,47]. The authors proposed that the active characteristics surrounding the tumor correlate with areas harboring occult microscopic illness based on the Grad-CAM attribution maps, in research for the categorization of lung cancer histology based on CT images [7].…”

Section: Literature On DL Models' Explainability In Medical Imagingmentioning

confidence: 99%

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

Nhlapho,

Atemkeng,

Brima

et al. 2024

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The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making mechanisms, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging ten state-of-the-art DL frameworks with transfer learning, we conducted a comprehensive evaluation encompassing both classification accuracy and interpretability. These models underwent thorough training, testing, and fine-tuning, resulting in EfficientNetB0, DenseNet121, and Xception outperforming the other models. These top-performing models were examined using adaptive path-based techniques to understand the underlying decision-making mechanisms. Grad-CAM and Grad-CAM++ highlighted critical image regions where the models identified patterns and features associated with each class of the brain tumor. The regions where the models identified patterns and features correspond visually to the regions where the tumors are located in the images. This result shows that DL models learn important features and patterns in the regions where tumors are located for decision-making.

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“…Supervised learning [ 5 , 6 ] estimates the confidence of multiple types of lesions in every CT slice, which is a two-dimensional image of a section of a body. However, this method requires CT slices and ground truth annotations in each slice (“slice-wise annotations”) as training data.…”

Section: Introductionmentioning

confidence: 99%

Automated screening of computed tomography using weakly supervised anomaly detection

et al. 2023

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Background Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, but while it reduces the annotation workload, it often suffers from lower performance. This study presents a novel weakly supervised anomaly detection (WSAD) algorithm trained based on scan-wise normal and anomalous annotations to provide better performance than conventional methods while reducing annotation workload . Methods Based on surveillance video anomaly detection methodology, feature vectors representing each CT slice were trained on an AR-Net-based convolutional network using a dynamic multiple-instance learning loss and a center loss function. The following two publicly available CT datasets were retrospectively analyzed: the RSNA brain hemorrhage dataset (normal scans: 12,862; scans with intracranial hematoma: 8882) and COVID-CT set (normal scans: 282; scans with COVID-19: 95). Results Anomaly scores of each slice were successfully predicted despite inaccessibility to any slice-wise annotations. Slice-level area under the curve (AUC), sensitivity, specificity, and accuracy from the brain CT dataset were 0.89, 0.85, 0.78, and 0.79, respectively. The proposed method reduced the number of annotations in the brain dataset by 97.1% compared to an ordinary slice-level supervised learning method. Conclusion This study demonstrated a significant annotation reduction in identifying anomalous CT slices compared to a supervised learning approach. The effectiveness of the proposed WSAD algorithm was verified through higher AUC than existing anomaly detection techniques. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-023-02965-4.

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Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning

Cited by 25 publications

References 34 publications

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

Automated screening of computed tomography using weakly supervised anomaly detection

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