Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy.
Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy.
Commun Biol. 2023 Mar 3;6(1):241. doi: 10.1038/s42003-023-04585-9.
One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager , is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities.
生物成像中的一个主要问题通常被严重低估，即提取用于区分或回归任务的特征是否仍然适用于更广泛的类似实验集，或者在图像采集过程中是否存在不可预测的干扰。当涉及到深度学习特征时，这个问题更加重要，因为黑盒描述符（深度特征）与研究中生物实体的表型特性之间缺乏先验的已知关系。在这方面，广泛使用描述符，如来自预训练卷积神经网络（CNNs）的描述符，受到它们缺乏明显物理意义且容易受到非特异性偏差的影响，即不依赖于细胞表型，而是依赖于采集伪影（如亮度或纹理变化、焦点偏移、自发荧光或光漂白）的特征的阻碍。所提出的 Deep-Manager 软件平台提供了一种有效选择对非特异性干扰敏感度较低且具有高区分能力的特征的可能性。Deep-Manager 可用于手工制作和深度特征的情况。该方法的前所未有的性能通过五个不同的案例研究得到了证明，范围从选择与化疗相关的乳腺癌细胞死亡研究中的手工绿色荧光蛋白强度特征，到解决与深度迁移学习相关的问题。Deep-Manager 可在 https://github.com/BEEuniroma2/Deep-Manager 上免费获得，适用于生物成像的许多领域，并设想随着新的图像采集干扰和模式的不断升级而进行更新。
