Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

Kuzina, Anna; Egorov, Evgenii; Burnaev, Evgeny

doi:10.3389/fnins.2019.00844

Cited by 21 publications

(9 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common approach to apply a prior-sharing strategy-and, in general, transfer learning-was fine-tuning all the parameters of a pretrained CNN [29,[31][32][33]35,39,71, (80% of all prior-sharing methods). Other approaches utilized Bayesian graphical models [37,38,120,121], graph neural networks [122], kernel methods [64,123], multilayer perceptrons [124], and Pearson-correlation methods [125]. Additionally, Sato et al [27] proposed a general framework to inhibit negative transfer.…”

Section: Parameter-based Approachesmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer’s diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

show abstract

Section: Parameter-based Approachesmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“… 224 In relation to standardization of QIB, an AI algorithm that automates the process of QIB extraction has the capability to decrease variability, such as through an automated pipeline that can reduce ambiguity and variability in lesion segmentation. 225 Extracting a QIB using AI in a fully automated manner is also feasible. For example, it can predict the functional flow reserve from cardiac CT data by point estimatioin.…”

Section: Artificial Intelligencementioning

confidence: 99%

Variability and Standardization of Quantitative Imaging

et al. 2020

View full text Add to dashboard Cite

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

show abstract

“…We can also use a special boundary loss from [20] and a fusion of multi-fidelity data [21] to increase semantic segmentation accuracy; sparse convolutions from [22] to increase computational efficiency. Another possibility is to apply bayesian generative models from [23] and latent convolutional manifolds from [24] for transfer learning of semantic segmentation tasks.…”

Section: Discussionmentioning

confidence: 99%

Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

Павлов

Artemov

Sharaev

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

Self Cite

View full text Add to dashboard Cite

Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain. In contrast, image-level annotations, where only the presence of lesion is marked, are generally cheap, generated in far larger volumes compared to pixel-level labels, and contain less labeling noise. In the context of brain tumor segmentation, both pixel-level and image-level annotations are commonly available; thus, a natural question arises whether a segmentation procedure could take advantage of both. In the present work we: 1) propose a learning-based framework that allows simultaneous usage of both pixel-and image-level annotations in MRI images to learn a segmentation model for brain tumor; 2) study the influence of comparative amounts of pixel-and image-level annotations on the quality of brain tumor segmentation; 3) compare our approach to the traditional fully-supervised approach and show that the performance of our method in terms of segmentation quality may be competitive.

show abstract

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

Cited by 21 publications

References 46 publications

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Variability and Standardization of Quantitative Imaging

Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

Contact Info

Product

Resources

About