• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권9호
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• pp.700-704
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• 2009

본 논문은 영상보정 및 다단계 정합을 통한 전립선의 MR 영상과 병리 영상 간의 융합방법을 제안한다. 제안 방법은 영상보정, 강체 정합, 비강체 정합, 영상융합의 네 단계로 이루어진다. 첫째, 영상보정 단계에서 T2 MR 강조 영상의 출혈 부위의 자기값을 T1 MR 강조 영상의 자기값으로 대체시키고, 2, 4장으로 분리된 병리 영상을 한장의 영상으로 만든 후 MR 영상과 동일한 해상도로 줄인다. 둘째, 전립선의 T2 MR 강조 영상과 병리 영상 간에 자기간의 상호정보를 최적화하는 강체변환을 구한다. 셋째, TPS 와핑을 이용하여 병리 영상의 전립선 부위가 T2 MR 강조 영상의 전립선 부위에 정합되는 비강체변환을 구한다. 넷째, MR 영상과 변환을 적용시킨 병리 영상을 융합한다. 실험 결과 영상보정 및 다단계 정합 후의 전립선의 T2 MR 강조 영상과 병리 영상의 간의 평균 거리 오차는 0.8815 mm였고, 두 영상의 융합을 통해 T2 MR 강조 영상에서 전립선 암의 위치를 정확하게 볼 수 있었다.

• Applied Microscopy
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• 제51권
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• pp.6.1-6.9
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• 2021

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promising statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

• Journal of Multimedia Information System
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• 제8권3호
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• pp.175-182
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• 2021

Oral cancer is ranked second most diagnosed cancer among Indian population and ranked sixth all around the world. Oral cancer is one of the deadliest cancers with high mortality rate and very less 5-year survival rates even after treatment. It becomes necessary to detect oral malignancies as early as possible so that timely treatment may be given to patient and increase the survival chances. In recent years deep learning based frameworks have been proposed by many researchers that can detect malignancies from medical images. In this paper we have proposed a deep learning-based framework which detects oral cancer from histopathology images very efficiently. We have designed our model to split the color channels and extract deep features from these individual channels rather than single combined channel with the help of Efficient NET B3. These features from different channels are fused by using feature fusion module designed as a layer and placed before dense layers of Efficient NET. The experiments were performed on our own dataset collected from hospitals. We also performed experiments of BreakHis, and ICML datasets to evaluate our model. The results produced by our model are very good as compared to previously reported results.

• Journal of Korean Neurosurgical Society
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• 제52권3호
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• pp.250-253
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• 2012

We report a rare case of cerebellar liponeurocytoma with an unusually aggressive histopathology. A 49-year-old man presented with a four-month history of headache, vertigo, and progressive swaying gait. Magnetic resonance imaging showed a $3{\times}3.5cm$ sized relatively well-demarcated round mass lesion in the fourth ventricle, characterized by high signal intensity on T2-weighted images. Postcontrast images revealed strong enhancement of the solid portion and the cyst wall. The patient underwent suboccipital craniectomy and tumor removal. The pathologic diagnosis was cerebellar liponeurocytoma. Adjuvant radiotherapy was offered due to concerns related to the high proliferative index (Ki-67, 13.68%) of the tumor. At the last routine postoperative follow-up visit (12 months), the patient complained of no specific symptom and there was no evidence of tumor recurrence. However, longterm follow-up and the analysis of similar cases are necessary because of the low number of reports and the short follow-up of cases.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Korean Journal of Radiology
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• 제22권8호
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• pp.1279-1288
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• 2021

Objective: To assess the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 treatment response algorithm (TRA) for the evaluation of hepatocellular carcinoma (HCC) treated with transarterial radioembolization. Materials and Methods: This retrospective study included patients who underwent transarterial radioembolization for HCC followed by hepatic surgery between January 2011 and December 2019. The resected lesions were determined to have either complete (100%) or incomplete (< 100%) necrosis based on histopathology. Three radiologists independently reviewed the CT or MR images of pre- and post-treatment lesions and assigned categories based on the LI-RADS version 2018 and the TRA, respectively. Diagnostic performances of LI-RADS treatment response (LR-TR) viable and nonviable categories were assessed for each reader, using histopathology from hepatic surgeries as a reference standard. Inter-reader agreements were evaluated using Fleiss κ. Results: A total of 27 patients (mean age ± standard deviation, 55.9 ± 9.1 years; 24 male) with 34 lesions (15 with complete necrosis and 19 with incomplete necrosis on histopathology) were included. To predict complete necrosis, the LR-TR nonviable category had a sensitivity of 73.3-80.0% and a specificity of 78.9-89.5%. For predicting incomplete necrosis, the LR-TR viable category had a sensitivity of 73.7-79.0% and a specificity of 93.3-100%. Five (14.7%) of 34 treated lesions were categorized as LR-TR equivocal by consensus, with two of the five lesions demonstrating incomplete necrosis. Interreader agreement for the LR-TR category was 0.81 (95% confidence interval: 0.66-0.96). Conclusion: The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.

• 한국멀티미디어학회논문지
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• 제23권4호
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• pp.539-548
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• 2020

Deep learning technology is currently being used and applied in many different fields. Convolution neural network (CNN) is a method of artificial neural networks in deep learning, which is commonly used for analyzing different types of images through classification. In the conventional classification of histopathology images of prostate carcinomas, the rating of cancer is classified by human subjective observation. However, this approach has produced to some misdiagnosing of cancer grading. To solve this problem, CNN based classification method is proposed in this paper, to train the histological images and classify the prostate cancer grading into two classes of the benign and malignant. The CNN architecture used in this paper is based on the VGG models, which is specialized for image classification. However, color normalization was performed based on the contrast enhancement technique, and the normalized images were used for CNN training, to compare the classification results of both original and normalized images. In all cases, accuracy was over 90%, accuracy of the original was 96%, accuracy of other cases was higher, and loss was the lowest with 9%.

• 한국멀티미디어학회논문지
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• 제24권7호
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• pp.849-859
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• 2021

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

• 한국컴퓨터그래픽스학회논문지
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• 제29권3호
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• pp.127-135
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• 2023

조직병리에서 전체 슬라이드 영상의 정확한 분할은 질병 진단과 치료 계획에 매우 중요한 작업이다. 그러나 전체 슬라이드 영상은 크기가 크고 조직의 형태, 염색 및 촬영 조건이 다양하기 때문에 기존의 자동 영상 분할 알고리즘을 항상 적용하는 것은 어렵다. 최근 인간의 전문 지식과 알고리즘을 결합한 대화형 영상 분할 기술의 발전은 전체 슬라이드 영상 분할의 효율 성과 정확성을 개선할 수 있는 가능성을 보여주었다. 그러나 이러한 접근 방식은 동시에 어려운 과제를 제기하기도 했다. 본 논문에서는 다중 해상도 전체 슬라이드 영상을 활용하는 새로운 대화형 분할 방법인 ZoomISEG를 제안한다. 기존의 단일 스케일 방법과의 비교 및 ablation study를 통해 제안된 방법의 효율성과 성능을 입증한다. 실험 결과, 제안된 방법은 사람의 개입을 줄이면서도 최고 해상도 데이터를 사용하는 방식에 필적하는 정확도를 달성함을 확인했다.