99 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
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
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
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
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
Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration
The registration of pathological images plays an important role in medical
applications. Despite its significance, most researchers in this field
primarily focus on the registration of normal tissue into normal tissue. The
negative impact of focal tissue, such as the loss of spatial correspondence
information and the abnormal distortion of tissue, are rarely considered. In
this paper, we propose GIRNet, a novel unsupervised approach for pathological
image registration by incorporating segmentation and inpainting through the
principles of Generation, Inpainting, and Registration (GIR). The registration,
segmentation, and inpainting modules are trained simultaneously in a
co-learning manner so that the segmentation of the focal area and the
registration of inpainted pairs can improve collaboratively. Overall, the
registration of pathological images is achieved in a completely unsupervised
learning framework. Experimental results on multiple datasets, including
Magnetic Resonance Imaging (MRI) of T1 sequences, demonstrate the efficacy of
our proposed method. Our results show that our method can accurately achieve
the registration of pathological images and identify lesions even in
challenging imaging modalities. Our unsupervised approach offers a promising
solution for the efficient and cost-effective registration of pathological
images. Our code is available at
https://github.com/brain-intelligence-lab/GIRNet.Comment: 13 pages, 7 figures, published in Medical Image Computing and
Computer Assisted Intervention (MICCAI) 202
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such as
ImageNet, COCO and KITTI have helped drive enormous improvements by enabling
researchers to understand the strengths and limitations of different algorithms
via performance comparison. However, this type of approach has had limited
translation to problems in robotic assisted surgery as this field has never
established the same level of common datasets and benchmarking methods. In 2015
a sub-challenge was introduced at the EndoVis workshop where a set of robotic
images were provided with automatically generated annotations from robot
forward kinematics. However, there were issues with this dataset due to the
limited background variation, lack of complex motion and inaccuracies in the
annotation. In this work we present the results of the 2017 challenge on
robotic instrument segmentation which involved 10 teams participating in
binary, parts and type based segmentation of articulated da Vinci robotic
instruments
Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
Recent advances in denoising diffusion probabilistic models have shown great
success in image synthesis tasks. While there are already works exploring the
potential of this powerful tool in image semantic segmentation, its application
in weakly supervised semantic segmentation (WSSS) remains relatively
under-explored. Observing that conditional diffusion models (CDM) is capable of
generating images subject to specific distributions, in this work, we utilize
category-aware semantic information underlied in CDM to get the prediction mask
of the target object with only image-level annotations. More specifically, we
locate the desired class by approximating the derivative of the output of CDM
w.r.t the input condition. Our method is different from previous diffusion
model methods with guidance from an external classifier, which accumulates
noises in the background during the reconstruction process. Our method
outperforms state-of-the-art CAM and diffusion model methods on two public
medical image segmentation datasets, which demonstrates that CDM is a promising
tool in WSSS. Also, experiment shows our method is more time-efficient than
existing diffusion model methods, making it practical for wider applications
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