10 research outputs found
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study
Computer-aided analysis of biological images typically requires extensive
training on large-scale annotated datasets, which is not viable in many
situations. In this paper we present GAN-DL, a Discriminator Learner based on
the StyleGAN2 architecture, which we employ for self-supervised image
representation learning in the case of fluorescent biological images. We show
that Wasserstein Generative Adversarial Networks combined with linear Support
Vector Machines enable high-throughput compound screening based on raw images.
We demonstrate this by classifying active and inactive compounds tested for the
inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to
previous methods, our deep learning based approach does not require any
annotation besides the one that is normally collected during the sample
preparation process. We test our technique on the RxRx19a Sars-CoV-2 image
collection. The dataset consists of fluorescent images that were generated to
assess the ability of regulatory-approved or in late-stage clinical trials
compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and
HRCE cell lines. We show that our technique can be exploited not only for
classification tasks, but also to effectively derive a dose response curve for
the tested treatments, in a self-supervised manner. Lastly, we demonstrate its
generalization capabilities by successfully addressing a zero-shot learning
task, consisting in the categorization of four different cell types of the
RxRx1 fluorescent images collection
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations
Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
Industry 4.0 involves the integration of digital technologies, such as IoT,
Big Data, and AI, into manufacturing and industrial processes to increase
efficiency and productivity. As these technologies become more interconnected
and interdependent, Industry 4.0 systems become more complex, which brings the
difficulty of identifying and stopping anomalies that may cause disturbances in
the manufacturing process. This paper aims to propose a diffusion-based model
for real-time anomaly prediction in Industry 4.0 processes. Using a
neuro-symbolic approach, we integrate industrial ontologies in the model,
thereby adding formal knowledge on smart manufacturing. Finally, we propose a
simple yet effective way of distilling diffusion models through Random Fourier
Features for deployment on an embedded system for direct integration into the
manufacturing process. To the best of our knowledge, this approach has never
been explored before.Comment: Accepted at the 26th Forum on specification and Design Languages (FDL
2023
High Resolution Explanation Maps for CNNs using Segmentation Networks
Recent developments have resulted in multiple techniques trying to explain how deep neural networks achieve their predictions. The explainability maps provided by such techniques are useful to understand what the network has learned and increase user confidence in critical applications such as the medical field or autonomous driving. Nonetheless, they typically have very low resolutions, severely limiting their capability of identifying finer details or multiple subjects. In this paper we employ an encoder-decoder architecture with skip connection known as U-Net, originally developed for segmenting medical images, as an image classifier and we show that state of the art explainable techniques applied to U-Net can generate pixel level explanation maps for images of any resolution
A Novel Proof-of-concept Framework for the Exploitation of ConvNets on Whole Slide Images
Traditionally, the analysis of histological samples is visually performed by a pathologist, who inspects under the microscope the tissue samples, looking for malignancies and anomalies. This visual assessment is both time consuming and highly unreliable due to the subjectivity of the evaluation. Hence, there are growing efforts towards the automatisation of such analysis, oriented to the development of computer-aided diagnostic tools, with a ever-growing role of techniques based on deep learning. In this work, we analyze some of the issues commonly associated with providing deep learning based techniques to medical professionals. We thus introduce a tool, aimed at both researchers and medical professionals, which simplifies and accelerates the training and exploitation of such models. The outcome of the tool is an attention map representing cancer probability distribution on top of the Whole Slide Image, driving the pathologist through a faster and more accurate diagnostic procedure
A novel proof-of-concept framework for the exploitation of ConvNets on Whole Slide Images
Traditionally, the analysis of histological samples is visually
performed by a pathologist, who inspects under the microscope the tissue
samples, looking for malignancies and anomalies. This visual assessment
is both time consuming and highly unreliable due to the subjectivity of
the evaluation. Hence, there are growing efforts towards the automati-
sation of such analysis, oriented to the development of computer-aided
diagnostic tools, with a ever-growing role of techniques based on deep
learning. In this work, we analyze some of the issues commonly associated
with providing deep learning based techniques to medical professionals.
We thus introduce a tool, aimed at both researchers and medical profes-
sionals, which simplifies and accelerates the training and exploitation of
such models. The outcome of the tool is an attention map representing
cancer probability distribution on top of the Whole Slide Image, driving
the pathologist through a faster and more accurate diagnostic procedure
Robotic Arm Dataset (RoAD): a Dataset to Support the Design and Test of Machine Learning-driven Anomaly Detection in a Production Line
The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community
Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line
The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community
Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before