neural network11 [upU-net] Benfenati, 2022, upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy # 세줄 요약 # 자동형광(auto-fluorescence)은 형광현미경(fluorescence microscopy) 영상에서 목표한 대상 물체를 관측하는데 장애물이 되는 아티팩트(artifact)를 만들어내기에, 저자들은 "upU-net"이라 명명한 U-net의 수정 모델을 가지고 딥러닝을 통해 이렇게 배경에서 방출된 아티팩트를 fluorescence confocal microscopy images에서 제거하는 연구를 수행하였다. upU-net을 학습하고 테스트하기 위하여 psf(point spread function)와 Perlin noise를 사용하여 실제 형광 현미경 영상과 거의 유사한 영상들을 만들어 실험을 하였고, 그 결과 배경이 제거되는 것은 물론이고 신경망이 영상을 재구축하면서 Guassi.. 2023. 2. 28. [FCN] Long et al., 2015, Fully Convolutional Networks for Semantic Segmentation # 세줄 요약 # 저자들이 소개한 FCN은 엔드 투 엔드(end-to-end), 픽셀에서 픽셀로(pixels-to-pixels) 학습되어, 즉 입력으로도 '영상'이 들어가고, 출력에서도 분할된 '영상'이 나오는 시멘틱 분할(semantic segmentation)에서 가장 높은 성능을 보인 합성곱 신경망이다. FCN의 핵심은 네트워크 이름에도 들어가 있듯이 'Fully convolutional Network'(완전 연결된 합성곱 신경망) 구조가 핵심 아이디어이며, 이를 구현하기 위해서 기존의 분류에 사용된 합성곱신경망 모델인 AlexNet, VGG, GoogLeNet 등을 기본모델(baseline model)로 사용하고, 이들 모델의 학습된 웨이트(weight)를 미세조정(fine-tuning)하여 분할.. 2022. 11. 24. [Review] Belthangady & Royer, 2019, Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. # 세줄 요약 # 딥러닝은 형광현미경법(fluorescence miroscopy)에서 영상 재구현(image reconstruction)을 하는데 중요한 툴이 되고 있다. 저자들은 영상 재구현과 초고해상도 이미징(super-resolution imaging)에서 가장 최신 기술의 적용 사례들을 리뷰하고, 가장 최근의 딥러닝 연구들이 영상 재구현 연구에 어떻게 적용되고 있는지 논의하고자 한다. 저자들은 학습데이터 수집, 영상에서 보이지 않는 구조의 재현 가능성, 재구현된 이미지의 위험성 등의 딥러닝을 사용할 때의 핵심 이슈들에 대해서도 논의하고 있다. # 상세 리뷰 # 1. 서론 형광현미경법(Fluorescence microscopy)은 생물학자들에게 있어 생물을 분자단위에서 생체구조와 작동 방식을 연구할.. 2022. 10. 9. [Review] Liu, Jin, et al., 2021, A survey on applications of deep learning in microscopy image analysis. # Three-line Summary # Microscopy images typically vary in signal-to-noise ratios and include a wealth of information that requires multiple parameters and time-consuming iterative algorithms for processing, but deep learning technologies develop quickly, and they have been applied in bioimage processing more and more frequently. This review article introduces the applications of deep learning a.. 2022. 9. 27. de Haan, Kevin, et al., 2020, Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy # Three-line Summary # In recent years, deep learning has been shown to be one of the leading machine learning techniques for a wide variety of inference tasks. In addition to its mainstream applications, such as classification, it has created transformative opportunities for image reconstruction and enhancement in optical microscopy. This article provides an overview of some of the recent work .. 2022. 9. 12. 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. [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. [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. Campanella et al., 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. # 세줄 요약 # The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. We present a multiple instance learning-based deep learning system that uses only reported diagnoses as labels for training. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph.. 2021. 10. 4. 이전 1 2 다음
