31 research outputs found
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning
Ultrasound imaging makes use of backscattering of waves during their
interaction with scatterers present in biological tissues. Simulation of
synthetic ultrasound images is a challenging problem on account of inability to
accurately model various factors of which some include intra-/inter scanline
interference, transducer to surface coupling, artifacts on transducer elements,
inhomogeneous shadowing and nonlinear attenuation. Current approaches typically
solve wave space equations making them computationally expensive and slow to
operate. We propose a generative adversarial network (GAN) inspired approach
for fast simulation of patho-realistic ultrasound images. We apply the
framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation
performed using pseudo B-mode ultrasound image simulator yields speckle mapping
of a digitally defined phantom. The stage I GAN subsequently refines them to
preserve tissue specific speckle intensities. The stage II GAN further refines
them to generate high resolution images with patho-realistic speckle profiles.
We evaluate patho-realism of simulated images with a visual Turing test
indicating an equivocal confusion in discriminating simulated from real. We
also quantify the shift in tissue specific intensity distributions of the real
and simulated images to prove their similarity.Comment: To appear in the Proceedings of the 2018 IEEE International Symposium
on Biomedical Imaging (ISBI 2018
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
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks
Convolutional Neural Networks have played a significant role in various
medical imaging tasks like classification and segmentation. They provide
state-of-the-art performance compared to classical image processing algorithms.
However, the major downside of these methods is the high computational
complexity, reliance on high-performance hardware like GPUs and the inherent
black-box nature of the model. In this paper, we propose quantised stand-alone
self-attention based models as an alternative to traditional CNNs. In the
proposed class of networks, convolutional layers are replaced with stand-alone
self-attention layers, and the network parameters are quantised after training.
We experimentally validate the performance of our method on classification and
segmentation tasks. We observe a reduction in model size,
lesser number of parameters, fewer FLOPs and more energy
efficiency during inference on CPUs. The code will be available at \href
{https://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neural-Network}{https://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neural-Network}.Comment: Accepted at MICCAI 2022 FAIR Worksho