Machine Learning7 Moen, Erick, et al., 2019, Deep learning for cellular image analysis # Three-line Summary # Deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. We review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We relay our labs'.. 2022. 9. 19. [Inception V3] Szegedy et al., 2016, Rethinking the Inception Architecture for Computer Vision # 세줄 요약 # Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. We are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge valida.. 2022. 4. 11. [ResNet] He et al., 2015, Deep Residual Learning for Image Recognition # 세줄 요약 # We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth (evaluate residual nets with a depth of up to 152 layers). This result won the 1st place on the.. 2021. 10. 17. Zhang et al., 2019, An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets # 세줄 요약 # Cancer has been one of the most threatening diseases, so our major goal is to identify malignant from benign lesions at radiology or CT imaging in the early stages, But it is difficult to collect such a large volume of images with pathological information. This paper explores two CNN models by focusing extensively on the expansion of training samples from two small pathologically prove.. 2021. 9. 27. Lindsay et al., 2018, Transfer Learning Approach to Predict Biopsy-Confirmed Malignancy of Lung Nodules from Imaging Data:A Pilot Study # 세줄요약 # Dataset Includes 796 patient have pathology-confirmed diagnosis(from CT-guided biopsy) and high-resolution CT imaging data at one institution between 2012 and 2017. To avoid overfitting on small dataset, Transfer learning to train a network using open dataset(LIDC) and added three new untrained layers. These study using only 86 patients, because Lesion location was manually determined u.. 2020. 10. 5. 김한웅 외., 2017, 폐 결절 검출을 위한 합성곱 신경망의 성능 개선 # 세줄요약 # 폐 영상 CAD(Computer Aided Detection) 시스템에서 결절과 비 결절(3mm 이상 결절, 3mm 이하 결절, 결절이 아닌 병변)로 구성된 ROI 영상을 분류하는 합성곱 신경망을 제안. LIDC 데이터셋을 사용하였으며, Hounsfiled Unit으로 변환하여 Window Setting으로 전처리한 이미지에서 방사선 전문의들의 판독결과를 바탕으로 ROI 영상 61404(Train Set: 52623, Test Set: 8781) 장을 추출. 총 14개의 레이어로 이루어진 합성곱 신경망(Convolution Layer = 8, Fully Connected Layer = 4)에 L1, L2 Nomalization, Data Augmentation 등을 사용하여 과적합을 완화.. 2020. 2. 5. Jo et al., 2019, Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data. # 세줄요약 # 최근 뇌영상 처리(Neuroimaging Techniques) 기법들의 빠른 발전과 MRI, PET 등 다양한 종류에서 방대한 양의 뇌영상 데이터들이 나오면서, 딥러닝을 이용하여 알츠하이머 질병의 조기 발견과 자동 분류를 해주는 진단 모델들이 연구 및 개발되고 있다. 이 논문에서는 그러한 알츠하이머 진단 모델들의 기존 연구들을 평가하고 정리하기 위해 총 16개의 논문들을 리뷰하였으며, 그 중 4개는 딥러닝과 함께 전통적인 머신러닝 기법을 사용한 연구들이고, 나머지 12개는 오직 딥러닝 기법만 사용한 연구들이다. 딥러닝 기법은 기존의 전통적인 머신러닝 기법에서 전제되는 특성 추출을 위한 복잡하고 어려운 뇌 영상 처리 기법들을 적용할 필요가 없기에, 현재 다양한 종류의 뇌영상(MRI, PET.. 2019. 10. 15. 이전 1 다음
