64 research outputs found
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos
Automated surgical step recognition is an important task that can
significantly improve patient safety and decision-making during surgeries.
Existing state-of-the-art methods for surgical step recognition either rely on
separate, multi-stage modeling of spatial and temporal information or operate
on short-range temporal resolution when learned jointly. However, the benefits
of joint modeling of spatio-temporal features and long-range information are
not taken in account. In this paper, we propose a vision transformer-based
approach to jointly learn spatio-temporal features directly from sequence of
frame-level patches. Our method incorporates a gated-temporal attention
mechanism that intelligently combines short-term and long-term spatio-temporal
feature representations. We extensively evaluate our approach on two cataract
surgery video datasets, namely Cataract-101 and D99, and demonstrate superior
performance compared to various state-of-the-art methods. These results
validate the suitability of our proposed approach for automated surgical step
recognition. Our code is released at:
https://github.com/nisargshah1999/GLSFormerComment: Accepted to MICCAI 2023 (Early Accept
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Source-free domain adaptation (SFDA) aims to adapt models trained on a
labeled source domain to an unlabeled target domain without the access to
source data. In medical imaging scenarios, the practical significance of SFDA
methods has been emphasized due to privacy concerns. Recent State-of-the-art
SFDA methods primarily rely on self-training based on pseudo-labels (PLs).
Unfortunately, PLs suffer from accuracy deterioration caused by domain shift,
and thus limit the effectiveness of the adaptation process. To address this
issue, we propose a Chebyshev confidence guided SFDA framework to accurately
assess the reliability of PLs and generate self-improving PLs for
self-training. The Chebyshev confidence is estimated by calculating probability
lower bound of the PL confidence, given the prediction and the corresponding
uncertainty. Leveraging the Chebyshev confidence, we introduce two
confidence-guided denoising methods: direct denoising and prototypical
denoising. Additionally, we propose a novel teacher-student joint training
scheme (TJTS) that incorporates a confidence weighting module to improve PLs
iteratively. The TJTS, in collaboration with the denoising methods, effectively
prevents the propagation of noise and enhances the accuracy of PLs. Extensive
experiments in diverse domain scenarios validate the effectiveness of our
proposed framework and establish its superiority over state-of-the-art SFDA
methods. Our paper contributes to the field of SFDA by providing a novel
approach for precisely estimating the reliability of pseudo-labels and a
framework for obtaining high-quality PLs, resulting in improved adaptation
performance
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
Medical image segmentation methods often rely on fully supervised approaches
to achieve excellent performance, which is contingent upon having an extensive
set of labeled images for training. However, annotating medical images is both
expensive and time-consuming. Semi-supervised learning offers a solution by
leveraging numerous unlabeled images alongside a limited set of annotated ones.
In this paper, we introduce a semi-supervised medical image segmentation method
based on the mean-teacher model, referred to as Dual-Decoder Consistency via
Pseudo-Labels Guided Data Augmentation (DCPA). This method combines consistency
regularization, pseudo-labels, and data augmentation to enhance the efficacy of
semi-supervised segmentation. Firstly, the proposed model comprises both
student and teacher models with a shared encoder and two distinct decoders
employing different up-sampling strategies. Minimizing the output discrepancy
between decoders enforces the generation of consistent representations, serving
as regularization during student model training. Secondly, we introduce mixup
operations to blend unlabeled data with labeled data, creating mixed data and
thereby achieving data augmentation. Lastly, pseudo-labels are generated by the
teacher model and utilized as labels for mixed data to compute unsupervised
loss. We compare the segmentation results of the DCPA model with six
state-of-the-art semi-supervised methods on three publicly available medical
datasets. Beyond classical 10\% and 20\% semi-supervised settings, we
investigate performance with less supervision (5\% labeled data). Experimental
outcomes demonstrate that our approach consistently outperforms existing
semi-supervised medical image segmentation methods across the three
semi-supervised settings
Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is
indispensable in medical image analysis and clinical research. A reliable and
effective reconstruction tool should: be fast in prediction of accurate well
localised and high resolution models, evaluate prediction uncertainty, work
with as little input data as possible. Current deep learning state of the art
(SOTA) 3D reconstruction methods, however, often only produce shapes of limited
variability positioned in a canonical position or lack uncertainty evaluation.
In this paper, we present a novel probabilistic deep learning approach for
concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric
uncertainty prediction. Our method is capable of reconstructing large surface
meshes from three quasi-orthogonal MR imaging slices from limited training sets
whilst modelling the location of each mesh vertex through a Gaussian
distribution. Prior shape information is encoded using a built-in linear
principal component analysis (PCA) model. Extensive experiments on cardiac MR
data show that our probabilistic approach successfully assesses prediction
uncertainty while at the same time qualitatively and quantitatively outperforms
SOTA methods in shape prediction. Compared to SOTA, we are capable of properly
localising and orientating the prediction via the use of a spatially aware
neural network.Comment: MICCAI 202
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Diffuse gliomas are malignant brain tumors that grow widespread through the
brain. The complex interactions between neoplastic cells and normal tissue, as
well as the treatment-induced changes often encountered, make glioma tumor
growth modeling challenging. In this paper, we present a novel end-to-end
network capable of generating future tumor masks and realistic MRIs of how the
tumor will look at any future time points for different treatment plans. Our
approach is based on cutting-edge diffusion probabilistic models and
deep-segmentation neural networks. We included sequential multi-parametric
magnetic resonance images (MRI) and treatment information as conditioning
inputs to guide the generative diffusion process. This allows for tumor growth
estimates at any given time point. We trained the model using real-world
postoperative longitudinal MRI data with glioma tumor growth trajectories
represented as tumor segmentation maps over time. The model has demonstrated
promising performance across a range of tasks, including the generation of
high-quality synthetic MRIs with tumor masks, time-series tumor segmentations,
and uncertainty estimates. Combined with the treatment-aware generated MRIs,
the tumor growth predictions with uncertainty estimates can provide useful
information for clinical decision-making.Comment: 13 pages, 10 figures, 2 tables, 2 agls, preprints in the IEEE trans.
format for submission to IEEE-TM
Test-time augmentation-based active learning and self-training for label-efficient segmentation
Deep learning techniques depend on large datasets whose annotation is
time-consuming. To reduce annotation burden, the self-training (ST) and
active-learning (AL) methods have been developed as well as methods that
combine them in an iterative fashion. However, it remains unclear when each
method is the most useful, and when it is advantageous to combine them. In this
paper, we propose a new method that combines ST with AL using Test-Time
Augmentations (TTA). First, TTA is performed on an initial teacher network.
Then, cases for annotation are selected based on the lowest estimated Dice
score. Cases with high estimated scores are used as soft pseudo-labels for ST.
The selected annotated cases are trained with existing annotated cases and ST
cases with border slices annotations. We demonstrate the method on MRI fetal
body and placenta segmentation tasks with different data variability
characteristics. Our results indicate that ST is highly effective for both
tasks, boosting performance for in-distribution (ID) and out-of-distribution
(OOD) data. However, while self-training improved the performance of
single-sequence fetal body segmentation when combined with AL, it slightly
deteriorated performance of multi-sequence placenta segmentation on ID data. AL
was helpful for the high variability placenta data, but did not improve upon
random selection for the single-sequence body data. For fetal body segmentation
sequence transfer, combining AL with ST following ST iteration yielded a Dice
of 0.961 with only 6 original scans and 2 new sequence scans. Results using
only 15 high-variability placenta cases were similar to those using 50 cases.
Code is available at: https://github.com/Bella31/TTA-quality-estimation-ST-ALComment: Accepted to MICCAI MILLanD workshop 202
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