• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

• Journal of Photoscience
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• 제9권2호
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• pp.5-8
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• 2002

Photoperiodism is an important adaptive phenomenon in various physiological parameters including reproduction to cope with seasonal changes. Involvement of extraretinal photoreceptors in the photoperiodism in non-mammalian vertebrates has been well established. In addition, circadian clock system is known to be involved in the photoperiodic time measurement. The pathway consists of light-input system, time measurement system (circadian clock), gonadotropin releasing hormone (GnRH) production in the hypothalamus, luteinizing hormone (LH) and follicle stimulating hormone (FSH) production in the pituitary, and final gonadal development. Recently, several laboratories reported photopigments newly cloned in the pineal, eyes and deep brain in addition to already known visual pigments in the retina. These are pinopsin, parapinopsin, VA-opsin, melanopsin, etc. All these photopigments belong to the opsin family having retinal as the chromophore. However, the function of these photopigments remains unknown. I reviewed the studies on the location of the photopigments by immunocytochemistry. I also discussed the results on the action spectra for induction of gonadal development in relation with the location of the photoreceptors. Various physiologically active substances distribute in the vertebrate brain. Such substances are GnRH, GnIH, neuropeptide Y, vasoactive intestinal peptide, c-Fos, galanin, neurosteroids, etc. I summarized the immunhistochemical studies on the distribution and the photoperiodic changes of these substances and discussed the route from the deep brain photoreceptor to GnRH cells.

• Annals of Clinical Neurophysiology
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• 제14권2호
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• pp.59-63
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• 2012

Epilepsy has continued to provide challenges to epileptologists, as a significant proportion of patients continue to suffer from seizures despite medical and surgical treatments. Deep brain stimulation (DBS) has emerged as a new therapeutic modality that has the potential to improve quality of life and occasionally be curative for patients with medically refractory epilepsy who are not surgical candidates. Several groups have used DBS in drug-resistant epilepsy cases for which resective surgery cannot be applied. The promising subcortical brain structures are anterior and centromedian nucleus of the thalamus, subthalamic nucleus, and other nuclei to treat epilepsy in light of previous clinical and experimental data. Recently two randomized trials of neurostimulation for controlling refractory epilepsy employed the strategies to stimulate electrodes placed on both anterior thalamic nuclei or near seizure foci in response to electroencephalographically detected epileptiform activity. However, the more large-scale, long-term clinical trials which elucidates optimal stimulation parameters, ideal selection criteria for epilepsy DBS should be performed before long. In order to continue to advance the frontier of this field, it is imperative to have a good grasp of the current body of knowledge.

• 대한임상검사과학회지
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• 제51권4호
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• pp.468-474
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• 2019

뇌심부자극술은 뇌심부핵에 고주파 전기자극을 전달하여 약물 불응성 이상운동질환을 치료하는 효과적인 방법이다. 그리고 미세전극기록은 뇌심부자극 수술 중에 MRI와 함께 뇌심부핵의 위치를 정확히 파악하여 수술 결과를 향상시키고 부작용을 최소화 할 수 있는 보조적인 검사이다. 본 논문의 목적은 이상운동질환에 대한 뇌심부자극 수술 중에 실시한 미세전극기록을 분석하여 신경생리학적 파형과 유용성을 알아보고자 하였다. 2018년 1월부터 12월까지 이상운동질환에 대한 뇌심부자극 수술 중에 미세전극기록를 실시한 환자 대상으로 후향적 조사를 하였다. 총 28명의 환자 중에 시상하핵은 38 개의 MER, 내측 담창구는 10개의 MER, 복내측 시상핵은 4개의 MER을 실시했다. 모두 목표지점을 찾았고 미세자극을 이용해서 부작용의 여부를 확인하고 목표지점을 재조정하였다. 수술 후 총 28명의 환자에서 모두 임상 증상은 호전되었다. 결론적으로. 미세전극기록은 신경생리학적 파형을 이용해서 MRI와 함께 정확한 뇌심부핵 부위를 파악해서 이상운동질환에 대한 뇌심부자극 수술 결과를 향상시키고 부작용을 최소화할 수 있는 유용한 검사이다.

• ETRI Journal
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• 제44권4호
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• 의료법학
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• 제23권2호
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• pp.119-139
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• 2022

뇌에 전기적·자기적 자극을 가하는 뇌자극기술은 신경학적·정신학적 장애에 대해 다양한 범위에 걸쳐 상당한 치료 가능성을 보여준다. 뇌자극기술은 침습 여부에 따라 침습적 기술과 비침습적 기술로 구분되는데, 뇌심부자극술(이하, DBS)은 대표적인 침습적 뇌자극기술에 속한다. 현재 DBS는 식약처 고시인 "의료기기 품목 및 품목별 등급에 관한 규정"에 따라 4등급 의료기기로 분류되어 몇몇 질환에서 안정된 치료법으로 사용되고 있다. 동시에 날로 그 기술이 발전하여 다양한 방향에서 이용방법이 논의되고 있다. 반면, 이와 관련한 법적 규제에 대한 논의는 상대적으로 적은 편이다. 이러한 배경에서 본 글은 DBS의 기술 및 효과와 안전성을 간략하게 소개한 이후, DBS 이용에서 고려할 수 있는 주요한 법적 쟁점을 이용 목적별로, 즉 치료목적, 임상연구 목적, 표준적 치료법이 아니나 다른 치료법이 없는 경우, 향상 목적으로 구분하여 논의하고, 어떠한 목적의 이용이든 DBS 이용에 따른 법적 책임의 문제에서 새로이 공통적으로 부상하고 있는 쟁점-위험·이익평가, 의사의 설명의무, 환자의 동의능력, 기기의 조정, 보험의 보장-을 소개하고 논의한다.

• Investigative Magnetic Resonance Imaging
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• 제25권4호
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• pp.293-299
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• 2021

Purpose: To accelerate magnetic resonance fingerprinting (MRF) by developing a flexible deep learning reconstruction method. Materials and Methods: Synthetic data were used to train a deep learning model. The trained model was then applied to MRF for different organs and diseases. Iterative reconstruction was performed outside the deep learning model, allowing a changeable encoding matrix, i.e., with flexibility of choice for image resolution, radiofrequency coil, k-space trajectory, and undersampling mask. In vivo experiments were performed on normal brain and prostate cancer volunteers to demonstrate the model performance and generalizability. Results: In 400-dynamics brain MRF, direct nonuniform Fourier transform caused a slight increase of random fluctuations on the T2 map. These fluctuations were reduced with the proposed method. In prostate MRF, the proposed method suppressed fluctuations on both T1 and T2 maps. Conclusion: The deep learning and iterative MRF reconstruction method described in this study was flexible with different acquisition settings such as radiofrequency coils. It is generalizable for different in vivo applications.