158 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
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
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
Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Organ at Risk (OAR) segmentation from CT scans is a key component of the
radiotherapy treatment workflow. In recent years, deep learning techniques have
shown remarkable potential in automating this process. In this paper, we
investigate the performance of Generative Adversarial Networks (GANs) compared
to supervised learning approaches for segmenting OARs from CT images. We
propose three GAN-based models with identical generator architectures but
different discriminator networks. These models are compared with
well-established CNN models, such as SE-ResUnet and DeepLabV3, using the
StructSeg dataset, which consists of 50 annotated CT scans containing contours
of six OARs. Our work aims to provide insight into the advantages and
disadvantages of adversarial training in the context of OAR segmentation. The
results are very promising and show that the proposed GAN-based approaches are
similar or superior to their CNN-based counterparts, particularly when
segmenting more challenging target organs
SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation
Microscopic image segmentation is a challenging task, wherein the objective
is to assign semantic labels to each pixel in a given microscopic image. While
convolutional neural networks (CNNs) form the foundation of many existing
frameworks, they often struggle to explicitly capture long-range dependencies.
Although transformers were initially devised to address this issue using
self-attention, it has been proven that both local and global features are
crucial for addressing diverse challenges in microscopic images, including
variations in shape, size, appearance, and target region density. In this
paper, we introduce SA2-Net, an attention-guided method that leverages
multi-scale feature learning to effectively handle diverse structures within
microscopic images. Specifically, we propose scale-aware attention (SA2) module
designed to capture inherent variations in scales and shapes of microscopic
regions, such as cells, for accurate segmentation. This module incorporates
local attention at each level of multi-stage features, as well as global
attention across multiple resolutions. Furthermore, we address the issue of
blurred region boundaries (e.g., cell boundaries) by introducing a novel
upsampling strategy called the Adaptive Up-Attention (AuA) module. This module
enhances the discriminative ability for improved localization of microscopic
regions using an explicit attention mechanism. Extensive experiments on five
challenging datasets demonstrate the benefits of our SA2-Net model. Our source
code is publicly available at \url{https://github.com/mustansarfiaz/SA2-Net}.Comment: BMVC 2023 accepted as ora
Improving the domain generalization and robustness of neural networks for medical imaging
Deep neural networks are powerful tools to process medical images, with great potential to accelerate clinical workflows and facilitate large-scale studies. However, in order to achieve satisfactory performance at deployment, these networks generally require massive labeled data collected from various domains (e.g., hospitals, scanners), which is rarely available in practice. The main goal of this work is to improve the domain generalization and robustness of neural networks for medical imaging when labeled data is limited.
First, we develop multi-task learning methods to exploit auxiliary data to enhance networks. We first present a multi-task U-net that performs image classification and MR atrial segmentation simultaneously. We then present a shape-aware multi-view autoencoder together with a multi-view U-net, which enables extracting useful shape priors from complementary long-axis views and short-axis views in order to assist the left ventricular myocardium segmentation task on the short-axis MR images. Experimental results show that the proposed networks successfully leverage complementary information from auxiliary tasks to improve model generalization on the main segmentation task.
Second, we consider utilizing unlabeled data. We first present an adversarial data augmentation method with bias fields to improve semi-supervised learning for general medical image segmentation tasks. We further explore a more challenging setting where the source and the target images are from different data distributions. We demonstrate that an unsupervised image style transfer method can bridge the domain gap, successfully transferring the knowledge learned from labeled balanced Steady-State Free Precession (bSSFP) images to unlabeled Late Gadolinium Enhancement (LGE) images, achieving state-of-the-art performance on a public multi-sequence cardiac MR segmentation challenge.
For scenarios with limited training data from a single domain, we first propose a general training and testing pipeline to improve cardiac image segmentation across various unseen domains. We then present a latent space data augmentation method with a cooperative training framework to further enhance model robustness against unseen domains and imaging artifacts.Open Acces
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation
Active learning algorithms have become increasingly popular for training
models with limited data. However, selecting data for annotation remains a
challenging problem due to the limited information available on unseen data. To
address this issue, we propose EdgeAL, which utilizes the edge information of
unseen images as {\it a priori} information for measuring uncertainty. The
uncertainty is quantified by analyzing the divergence and entropy in model
predictions across edges. This measure is then used to select superpixels for
annotation. We demonstrate the effectiveness of EdgeAL on multi-class Optical
Coherence Tomography (OCT) segmentation tasks, where we achieved a 99% dice
score while reducing the annotation label cost to 12%, 2.3%, and 3%,
respectively, on three publicly available datasets (Duke, AROI, and UMN). The
source code is available at \url{https://github.com/Mak-Ta-Reque/EdgeAL}Comment: This version of the contribution has been submitted in miccai202
Self-training with dual uncertainty for semi-supervised medical image segmentation
In the field of semi-supervised medical image segmentation, the shortage of
labeled data is the fundamental problem. How to effectively learn image
features from unlabeled images to improve segmentation accuracy is the main
research direction in this field. Traditional self-training methods can
partially solve the problem of insufficient labeled data by generating pseudo
labels for iterative training. However, noise generated due to the model's
uncertainty during training directly affects the segmentation results.
Therefore, we added sample-level and pixel-level uncertainty to stabilize the
training process based on the self-training framework. Specifically, we saved
several moments of the model during pre-training, and used the difference
between their predictions on unlabeled samples as the sample-level uncertainty
estimate for that sample. Then, we gradually add unlabeled samples from easy to
hard during training. At the same time, we added a decoder with different
upsampling methods to the segmentation network and used the difference between
the outputs of the two decoders as pixel-level uncertainty. In short, we
selectively retrained unlabeled samples and assigned pixel-level uncertainty to
pseudo labels to optimize the self-training process. We compared the
segmentation results of our model with five semi-supervised approaches on the
public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method
achieves better segmentation performance on both datasets under the same
settings, demonstrating its effectiveness, robustness, and potential
transferability to other medical image segmentation tasks. Keywords: Medical
image segmentation, semi-supervised learning, self-training, uncertainty
estimatio
Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation
The Diffusion Probabilistic Model (DPM) has emerged as a highly effective
generative model in the field of computer vision. Its intermediate latent
vectors offer rich semantic information, making it an attractive option for
various downstream tasks such as segmentation and detection. In order to
explore its potential further, we have taken a step forward and considered a
more complex scenario in the medical image domain, specifically, under an
unsupervised adaptation condition. To this end, we propose a Diffusion-based
and Prototype-guided network (DP-Net) for unsupervised domain adaptive
segmentation. Concretely, our DP-Net consists of two stages: 1) Distribution
Aligned Diffusion (DADiff), which involves training a domain discriminator to
minimize the difference between the intermediate features generated by the DPM,
thereby aligning the inter-domain distribution; and 2) Prototype-guided
Consistency Learning (PCL), which utilizes feature centroids as prototypes and
applies a prototype-guided loss to ensure that the segmentor learns consistent
content from both source and target domains. Our approach is evaluated on
fundus datasets through a series of experiments, which demonstrate that the
performance of the proposed method is reliable and outperforms state-of-the-art
methods. Our work presents a promising direction for using DPM in complex
medical image scenarios, opening up new possibilities for further research in
medical imaging
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