Automatic prediction of fluorescently labeled organelles from label-free
transmitted light input images is an important, yet difficult task. The
traditional way to obtain fluorescence images is related to performing
biochemical labeling which is time-consuming and costly. Therefore, an
automatic algorithm to perform the task based on the label-free transmitted
light microscopy could be strongly beneficial. The importance of the task
motivated researchers from the France-BioImaging to organize the LightMyCells
challenge where the goal is to propose an algorithm that automatically predicts
the fluorescently labeled nucleus, mitochondria, tubulin, and actin, based on
the input consisting of bright field, phase contrast, or differential
interference contrast microscopic images. In this work, we present the
contribution of the AGHSSO team based on a carefully prepared and trained
encoder-decoder deep neural network that achieves a considerable score in the
challenge, being placed among the best-performing teams.

自动预测无标记透射光输入图像中的荧光标记细胞器是一项重要且困难的任务，本研究介绍了 AGHSSO 团队基于经过精心准备和训练的编码 - 解码深度神经网络在 LightMyCells 挑战中所取得的相当高得分，位居最佳表现团队之一。

基于块状编码器 - 解码器结构的自动透射光到荧光成像的转换：对 LightMyCells 挑战的贡献

Patch-Based Encoder-Decoder Architecture for Automatic Transmitted Light  to Fluorescence Imaging Transition: Contribution to the LightMyCells  Challenge

We present a recurrent encoder-decoder deep neural network architecture that
directly translates speech in one language into text in another. The model does
not explicitly transcribe the speech into text in the source language, nor does
it require supervision from the ground truth source language transcription
during training. We apply a slightly modified sequence-to-sequence with
attention architecture that has previously been used for speech recognition and
show that it can be repurposed for this more complex task, illustrating the
power of attention-based models. A single model trained end-to-end obtains
state-of-the-art performance on the Fisher Callhome Spanish-English speech
translation task, outperforming a cascade of independently trained
sequence-to-sequence speech recognition and machine translation models by 1.8
BLEU points on the Fisher test set. In addition, we find that making use of the
training data in both languages by multi-task training sequence-to-sequence
speech translation and recognition models with a shared encoder network can
improve performance by a further 1.4 BLEU points.

我们提出一种递归编码器 - 解码器深度神经网络架构，直接将一种语言中的语音转换为另一种语言中的文本，通过多任务训练序列到序列的语音翻译和识别模型通过共享编码器网络来提高性能。