Journal ArticleDOI
DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening.
TLDR
Wang et al. as discussed by the authors proposed a multi-task learning with adversarial losses to generate more accurate and realistic microscopy images using only a single bright-field image and the corresponding fluorescence images as a set of image pairs for training an end-to-end deep CNN.About:
This article is published in Medical Image Analysis.The article was published on 2021-02-12. It has received 9 citations till now. The article focuses on the topics: Image conversion & Image translation.read more
Citations
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Journal ArticleDOI
Deep-learning-based bright-field image generation from a single hologram using an unpaired dataset.
TL;DR: In this paper, the authors adopted an unpaired neural network training technique, namely CycleGAN, to generate bright-field microscope-like images from hologram reconstructions, which results in sharper and more realistic object reconstructions compared to the baseline paired setting.
Book ChapterDOI
Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
TL;DR: Multi-StyleGAN as discussed by the authors is a generative adversarial network that synthesises a multi-domain sequence of consecutive timesteps for time-lapse fluorescence microscopy images of living cells.
Book ChapterDOI
Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
TL;DR: Multi-styleGAN as mentioned in this paper is a generative adversarial network that synthesises a multi-domain sequence of consecutive timesteps for time-lapse fluorescence microscopy images of living cells.
Posted ContentDOI
CellDeathPred: A Deep Learning framework for Ferroptosis and Apoptosis prediction based on cell painting
Kenji Schorpp,Alaa Bessadok,Aidin Biibosunov,Ina Rothenaigner,Stefanie Strasser,Tingying Peng,Kamyar Hadian +6 more
TL;DR: In this article , a deep learning framework was proposed to distinguish cells undergoing ferroptosis or apoptosis from healthy cells using high-content-imaging based on cell painting.
Posted ContentDOI
Deep learning-based algorithm for predicting the live birth potential of mouse embryos
Yuta Tokuoka,Takahiro G. Yamada,Daisuke Mashiko,Zenki Ikeda,T. Kobayashi,Kazuo Yamagata,Akira Funahashi +6 more
TL;DR: In this article, a Normalized Multi-View Attention Network (NVAN) was developed to directly predict live birth potential from nuclear structural features in live-cell fluorescence images taken of mouse embryos across a wide range of stages.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.