Machine learning: applications of artificial intelligence to imaging and diagnosis

Nichols, James A.; Chan, Hsien W. Herbert; Baker, Matthew A. B.

doi:10.1007/s12551-018-0449-9

Cited by 269 publications

(173 citation statements)

References 29 publications

(31 reference statements)

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“…granulomatous vs. non-granulomatous) and their relationship to gene transcriptional changes will require more in-depth analysis than that reported here, using new techniques to integrate image analysis with complex “omics” and other meta data. Such approaches are fuelling significant advances in precision medicine in other fields, notably cancer and neuroscience 78– 80 , but have to date had limited application in the field of infectious disease. To facilitate the development of such approaches in leishmaniasis, we have generated a digital whole slide collection of the tissue sections generated in this study, stained with both H&E and markers to identify myeloid cells (F4/80 and 1A8).…”

Section: Discussionmentioning

confidence: 99%

Tissue and host species-specific transcriptional changes in models of experimental visceral leishmaniasis

Ashwin¹,

Seifert²,

Forrester³

et al. 2018

Wellcome Open Res

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Background: Human visceral leishmaniasis, caused by infection with Leishmania donovani or L. infantum, is a potentially fatal disease affecting 50,000-90,000 people yearly in 75 disease endemic countries, with more than 20,000 deaths reported. Experimental models of infection play a major role in understanding parasite biology, host-pathogen interaction, disease pathogenesis, and parasite transmission. In addition, they have an essential role in the identification and pre-clinical evaluation of new drugs and vaccines. However, our understanding of these models remains fragmentary. Although the immune response to Leishmania donovani infection in mice has been extensively characterized, transcriptomic analysis capturing the tissue-specific evolution of disease has yet to be reported. Methods: We provide an analysis of the transcriptome of spleen, liver and peripheral blood of BALB/c mice infected with L. donovani. Where possible, we compare our data in murine experimental visceral leishmaniasis with transcriptomic data in the public domain obtained from the study of L. donovani-infected hamsters and patients with human visceral leishmaniasis. Digitised whole slide images showing the histopathology in spleen and liver are made available via a dedicated website, www.leishpathnet.org. Results: Our analysis confirms marked tissue-specific alterations in the transcriptome of infected mice over time and identifies previously unrecognized parallels and differences between murine, hamster and human responses to infection. We show commonality of interferon-regulated genes whilst confirming a greater activation of type 2 immune pathways in infected hamsters compared to mice. Cytokine genes and genes encoding immune checkpoints were markedly tissue specific and dynamic in their expression, and pathways focused on non-immune cells reflected tissue specific immunopathology. Our data also addresses the value of measuring peripheral blood transcriptomics as a potential window into underlying systemic disease. Conclusions: Our transcriptomic data, coupled with histopathologic analysis of the tissue response, provide an additional resource to underpin future mechanistic studies and to guide clinical research.

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Section: Discussionmentioning

confidence: 99%

Tissue and host species-specific transcriptional changes in models of experimental visceral leishmaniasis

Ashwin¹,

Seifert²,

Forrester³

et al. 2018

Wellcome Open Res

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“…An image can be broken down into motifs, or a collection of pixels that form a basic unit of analysis. The first few layers of the CNN compare each part of an input image against some small sub-image [5]. Each node is assigned a certain feature (e.g., color, shape, size, etc.…”

Section: Neural Network and Deep Learningmentioning

confidence: 99%

“…Each node is assigned a certain feature (e.g., color, shape, size, etc. ), and the node's output to the next layer depends on how much a part of the image resembles the feature, a process performed by convolution [5]. After these convolutional layers, pooling layers, which are a standard NN, classify the overall image [5].…”

Section: Neural Network and Deep Learningmentioning

confidence: 99%

“…), and the node's output to the next layer depends on how much a part of the image resembles the feature, a process performed by convolution [5]. After these convolutional layers, pooling layers, which are a standard NN, classify the overall image [5]. CNNs first showed promise for medical image classification at the historic 2012 ImageNet Large Scale Visual Recognition (ILSVRC) conference.…”

Section: Neural Network and Deep Learningmentioning

confidence: 99%

“…Translational research in dermatology is already abundant, with data from the genome, epigenome, transcriptome, proteome, and microbiome, areas of research that are often referred to by the shortened term ''omics'' [2]. Recent advancements in faster processing and cheaper storage have allowed for the development of machine learning (ML) algorithms with human-like intelligence that have numerous applications in dermatology [3][4][5]. To assess the effectiveness of these emerging technologies, it is imperative that dermatologists have a basic understanding of artificial intelligence and ML.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

165

108

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information

Yang

Buehler

2023

Advanced Materials

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Solving materials engineering tasks is often hindered by limited information, such as in inverse problems with only boundary data information or design tasks with a simple objective but a vast search space. To address these challenges, multiple deep learning (DL) architectures are leveraged to predict missing mechanical information given limited known data in part of the domain, and further characterize the composite geometries from the recovered mechanical fields for 2D and 3D complex microstructures. In 2D, a conditional generative adversarial network (GAN) is utilized to complete partially masked field maps and predict the composite geometry with convolutional models with great accuracy and generality by making precise predictions on field data with mixed stress/strain components, hierarchical geometries, distinct materials properties and various types of microstructures including ill‐posed inverse problems. In 3D, a Transformer‐based architecture is implemented to predict complete 3D mechanical fields from input field snapshots. The model manifests excellent performance regardless of microstructural complexity and recovers the entire bulk field even from a single surface field image, allowing internal structural characterization from only boundary measurements. The whole frameworks provide efficient ways for analysis and design with incomplete information and allow the direct inverse translation from properties back to materials structures.

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Machine learning: applications of artificial intelligence to imaging and diagnosis

Cited by 269 publications

References 29 publications

Tissue and host species-specific transcriptional changes in models of experimental visceral leishmaniasis

Tissue and host species-specific transcriptional changes in models of experimental visceral leishmaniasis

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information

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