2 research outputs found
Communal Domain Learning for Registration in Drifted Image Spaces
Designing a registration framework for images that do not share the same
probability distribution is a major challenge in modern image analytics yet
trivial task for the human visual system (HVS). Discrepancies in probability
distributions, also known as \emph{drifts}, can occur due to various reasons
including, but not limited to differences in sequences and modalities (e.g.,
MRI T1-T2 and MRI-CT registration), or acquisition settings (e.g., multisite,
inter-subject, or intra-subject registrations). The popular assumption about
the working of HVS is that it exploits a communal feature subspace exists
between the registering images or fields-of-view that encompasses key
drift-invariant features. Mimicking the approach that is potentially adopted by
the HVS, herein, we present a representation learning technique of this
invariant communal subspace that is shared by registering domains. The proposed
communal domain learning (CDL) framework uses a set of hierarchical nonlinear
transforms to learn the communal subspace that minimizes the probability
differences and maximizes the amount of shared information between the
registering domains. Similarity metric and parameter optimization calculations
for registration are subsequently performed in the drift-minimized learned
communal subspace. This generic registration framework is applied to register
multisequence (MR: T1, T2) and multimodal (MR, CT) images. Results demonstrated
generic applicability, consistent performance, and statistically significant
improvement for both multi-sequence and multi-modal data using the proposed
approach (-value; Wilcoxon rank sum test) over baseline methods.Comment: MLMI-201
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review
Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks
of interest. In MRI, transfer learning is important for developing strategies
that address the variation in MR images. Additionally, transfer learning is
beneficial to re-utilize machine learning models that were trained to solve
related tasks to the task of interest. Our goal is to identify research
directions, gaps of knowledge, applications, and widely used strategies among
the transfer learning approaches applied in MR brain imaging. We performed a
systematic literature search for articles that applied transfer learning to MR
brain imaging. We screened 433 studies and we categorized and extracted
relevant information, including task type, application, and machine learning
methods. Furthermore, we closely examined brain MRI-specific transfer learning
approaches and other methods that tackled privacy, unseen target domains, and
unlabeled data. We found 129 articles that applied transfer learning to brain
MRI tasks. The most frequent applications were dementia related classification
tasks and brain tumor segmentation. A majority of articles utilized transfer
learning on convolutional neural networks (CNNs). Only few approaches were
clearly brain MRI specific, considered privacy issues, unseen target domains or
unlabeled data. We proposed a new categorization to group specific, widely-used
approaches. There is an increasing interest in transfer learning within brain
MRI. Public datasets have contributed to the popularity of Alzheimer's
diagnostics/prognostics and tumor segmentation. 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 to other
approaches.Comment: Accepted in Journal of Imagin