672 research outputs found
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.Comment: Accepted as a conference paper at IEEE EMBC, 201
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective
The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care
IterMiUnet: A lightweight architecture for automatic blood vessel segmentation
The automatic segmentation of blood vessels in fundus images can help analyze
the condition of retinal vasculature, which is crucial for identifying various
systemic diseases like hypertension, diabetes, etc. Despite the success of Deep
Learning-based models in this segmentation task, most of them are heavily
parametrized and thus have limited use in practical applications. This paper
proposes IterMiUnet, a new lightweight convolution-based segmentation model
that requires significantly fewer parameters and yet delivers performance
similar to existing models. The model makes use of the excellent segmentation
capabilities of Iternet architecture but overcomes its heavily parametrized
nature by incorporating the encoder-decoder structure of MiUnet model within
it. Thus, the new model reduces parameters without any compromise with the
network's depth, which is necessary to learn abstract hierarchical concepts in
deep models. This lightweight segmentation model speeds up training and
inference time and is potentially helpful in the medical domain where data is
scarce and, therefore, heavily parametrized models tend to overfit. The
proposed model was evaluated on three publicly available datasets: DRIVE,
STARE, and CHASE-DB1. Further cross-training and inter-rater variability
evaluations have also been performed. The proposed model has a lot of potential
to be utilized as a tool for the early diagnosis of many diseases
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