Deep Learning30 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. Chamier et al., 2019, Artificial intelligence for microscopy: what you should know # Three-line Summary # Artificial Intelligence based on Deep Learning (DL) is opening new horizons in biomedical research and promises to revolutionize the microscopy field. We introduce recent developments in DL applied to microscopy in a manner accessible to non-experts. We discuss how DL shows an outstanding potential to push the limits of microscopy, enhancing resolution, signal, and informa.. 2022. 9. 2. [U-Net] Ronneberger et al., 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation. # Three-line Summary # We present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. Using the network trained on transmitted light microscopy images, we won the ISBI cell trac.. 2022. 8. 11. [BGnet] Mockl et al., 2020, Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet # 세줄 요약 # 형광현미경법에서 연구자가 원치 않는 임의의 공간 형태에서 나오는 배경 형광은 광학현미경 영상의 품질을 저하시키는 주요 원인 중의 하나이다. 저자들은 현미경 영상에서 배경을 빠르게 추정하여, 영상 내의 포인트 소스(point source)를 뛰어난 정확도로 측정하기 위해, 깊은 신경망(deep neural network) 중에 하나인 U-net 형태의 구조를 기반으로 하는 BGnet을 개발하였다. 저자들은 잘 학습된 BGnet으로 배경을 측정 후 제거함으로서 영상 내의 다양한 PSF(point source function)들을 추출할 수 있었고, 이렇게 배경을 제거한 영상들을 사용하여 정밀한 생체구조를 볼 수 있는 고품질의 초해상도 영상을 재구현하였다. # 상세 리뷰 # 1. Introd.. 2022. 7. 19. [ANNA-PALM] Ouyang et al., 2018, Deep learning massively accelerates super-resolution localization microscopy # 세줄 요약 # 초고해상도 현미경법(Super-resolution microscopy method)의 영상 획득 속도는 단일 분자의 위치 결정(single-molecule localization)에 영향을 받게 되는데, 예를 들어 PALM과 STORM의 경우, 한번에 약 수십개 정도의 적은 수의 분자들만이 관측되는 단일 분자 영상 수천장을 합성하여 만들게 된다. 저자들은 최근에 컴퓨터 비전 분야에 각광받는 인공 신경망(Artificial Neural Network; ANN)을 사용하여, 훨씬 적은 수의 단일 분자 영상들과 widefield 영상를 가지고 초고해상도 형광현미경 영상(fluorescence image)을 재구현(reconstruction)하는 ANNA-PALM 모델을 소개하였다. 이 ANN.. 2022. 6. 22. [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. [MobileNet] Howard et al., 2017, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications # 세줄 요약 # MobileNets are based on a streamlined architecture that uses depthwise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet clas.. 2022. 3. 21. [DenseNet] Huang et al., 2017, Densely Connected Convolutional Networks # 세줄 요약 # We introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. For each layer, the feature-maps of all preceding layers are used as inputs, and their own feature-maps are used as inputs into all subsequent layers. DenseNet has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feat.. 2022. 3. 13. [공저자] Lee et al., 2021, Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network # 세줄 요약 # We created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Three types of models were used, depending on their structure: The ac.. 2021. 10. 25. Hwang et al, 2019, Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs. # 세줄 요약 # To develop a deep learning-based algorithm that can classify normal and abnormal results from chest radiographs with major thoracic diseases (pulmonary malignant neoplasm, active tuberculosis, pneumonia, pneumothorax). This diagnostic study developed a deep learning-based algorithm using single-center data (chest radiographs: 54,221 normal findings; 35,613 abnormal findings) and extern.. 2021. 10. 20. 이전 1 2 3 다음
