22 research outputs found
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive task. As it has to compute million of
parameters, it results to huge memory consumption. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity.
With the introduction of Fully Convolutional Neural Network, which uses finer
strides and utilizes deconvolutional layers for upsampling, it has been a go to
for any image segmentation task. In this paper, we propose two segmentation
architecture which not only needs one-third the parameters to compute but also
gives better accuracy than the similar architectures. The model weights were
transferred from the popular neural net like VGG19 and VGG16 which were trained
on Imagenet classification data-set. Then we transform all the fully connected
layers to convolutional layers and use dilated convolution for decreasing the
parameters. Lastly, we add finer strides and attach four skip architectures
which are element-wise summed with the deconvolutional layers in steps. We
train and test on different sparse and fine data-sets like Pascal VOC2012,
Pascal-Context and NYUDv2 and show how better our model performs in this tasks.
On the other hand our model has a faster inference time and consumes less
memory for training and testing on NVIDIA Pascal GPUs, making it more efficient
and less memory consuming architecture for pixel-wise segmentation.Comment: 8 page
Deep Representation Learning with Limited Data for Biomedical Image Synthesis, Segmentation, and Detection
Biomedical imaging requires accurate expert annotation and interpretation that can aid medical staff and clinicians in automating differential diagnosis and solving underlying health conditions. With the advent of Deep learning, it has become a standard for reaching expert-level performance in non-invasive biomedical imaging tasks by training with large image datasets. However, with the need for large publicly available datasets, training a deep learning model to learn intrinsic representations becomes harder. Representation learning with limited data has introduced new learning techniques, such as Generative Adversarial Networks, Semi-supervised Learning, and Self-supervised Learning, that can be applied to various biomedical applications. For example, ophthalmologists use color funduscopy (CF) and fluorescein angiography (FA) to diagnose retinal degenerative diseases. However, fluorescein angiography requires injecting a dye, which can create adverse reactions in the patients. So, to alleviate this, a non-invasive technique needs to be developed that can translate fluorescein angiography from fundus images. Similarly, color funduscopy and optical coherence tomography (OCT) are also utilized to semantically segment the vasculature and fluid build-up in spatial and volumetric retinal imaging, which can help with the future prognosis of diseases. Although many automated techniques have been proposed for medical image segmentation, the main drawback is the model's precision in pixel-wise predictions. Another critical challenge in the biomedical imaging field is accurately segmenting and quantifying dynamic behaviors of calcium signals in cells. Calcium imaging is a widely utilized approach to studying subcellular calcium activity and cell function; however, large datasets have yielded a profound need for fast, accurate, and standardized analyses of calcium signals. For example, image sequences from calcium signals in colonic pacemaker cells ICC (Interstitial cells of Cajal) suffer from motion artifacts and high periodic and sensor noise, making it difficult to accurately segment and quantify calcium signal events. Moreover, it is time-consuming and tedious to annotate such a large volume of calcium image stacks or videos and extract their associated spatiotemporal maps. To address these problems, we propose various deep representation learning architectures that utilize limited labels and annotations to address the critical challenges in these biomedical applications. To this end, we detail our proposed semi-supervised, generative adversarial networks and transformer-based architectures for individual learning tasks such as retinal image-to-image translation, vessel and fluid segmentation from fundus and OCT images, breast micro-mass segmentation, and sub-cellular calcium events tracking from videos and spatiotemporal map quantification. We also illustrate two multi-modal multi-task learning frameworks with applications that can be extended to other domains of biomedical applications. The main idea is to incorporate each of these as individual modules to our proposed multi-modal frameworks to solve the existing challenges with 1) Fluorescein angiography synthesis, 2) Retinal vessel and fluid segmentation, 3) Breast micro-mass segmentation, and 4) Dynamic quantification of calcium imaging datasets
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce
the number of accidents happening world-wide. With the arrival of Self-driving
cars it has become a staple challenge to solve the automatic recognition of
Traffic and Hand-held signs in the major streets. Various machine learning
techniques like Random Forest, SVM as well as deep learning models has been
proposed for classifying traffic signs. Though they reach state-of-the-art
performance on a particular data-set, but fall short of tackling multiple
Traffic Sign Recognition benchmarks. In this paper, we propose a novel and
one-for-all architecture that aces multiple benchmarks with better overall
score than the state-of-the-art architectures. Our model is made of residual
convolutional blocks with hierarchical dilated skip connections joined in
steps. With this we score 99.33% Accuracy in German sign recognition benchmark
and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,
we propose a newly devised dilated residual learning representation technique
which is very low in both memory and computational complexity
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Noisy data and the similarity in the ocular appearances caused by different
ophthalmic pathologies pose significant challenges for an automated expert
system to accurately detect retinal diseases. In addition, the lack of
knowledge transferability and the need for unreasonably large datasets limit
clinical application of current machine learning systems. To increase
robustness, a better understanding of how the retinal subspace deformations
lead to various levels of disease severity needs to be utilized for
prioritizing disease-specific model details. In this paper we propose the use
of disease-specific feature representation as a novel architecture comprised of
two joint networks -- one for supervised encoding of disease model and the
other for producing attention maps in an unsupervised manner to retain disease
specific spatial information. Our experimental results on publicly available
datasets show the proposed joint-network significantly improves the accuracy
and robustness of state-of-the-art retinal disease classification networks on
unseen datasets.Comment: \c{opyright} 2020 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Diagnosing different retinal diseases from Spectral Domain Optical Coherence
Tomography (SD-OCT) images is a challenging task. Different automated
approaches such as image processing, machine learning and deep learning
algorithms have been used for early detection and diagnosis of retinal
diseases. Unfortunately, these are prone to error and computational
inefficiency, which requires further intervention from human experts. In this
paper, we propose a novel convolution neural network architecture to
successfully distinguish between different degeneration of retinal layers and
their underlying causes. The proposed novel architecture outperforms other
classification models while addressing the issue of gradient explosion. Our
approach reaches near perfect accuracy of 99.8% and 100% for two separately
available Retinal SD-OCT data-set respectively. Additionally, our architecture
predicts retinal diseases in real time while outperforming human
diagnosticians.Comment: 8 pages. Accepted to 18th IEEE International Conference on Machine
Learning and Applications (ICMLA 2019
Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology
The rapid accessibility of portable and affordable retinal imaging devices
has made early differential diagnosis easier. For example, color funduscopy
imaging is readily available in remote villages, which can help to identify
diseases like age-related macular degeneration (AMD), glaucoma, or pathological
myopia (PM). On the other hand, astronauts at the International Space Station
utilize this camera for identifying spaceflight-associated neuro-ocular
syndrome (SANS). However, due to the unavailability of experts in these
locations, the data has to be transferred to an urban healthcare facility (AMD
and glaucoma) or a terrestrial station (e.g, SANS) for more precise disease
identification. Moreover, due to low bandwidth limits, the imaging data has to
be compressed for transfer between these two places. Different super-resolution
algorithms have been proposed throughout the years to address this.
Furthermore, with the advent of deep learning, the field has advanced so much
that x2 and x4 compressed images can be decompressed to their original form
without losing spatial information. In this paper, we introduce a novel model
called Swin-FSR that utilizes Swin Transformer with spatial and depth-wise
attention for fundus image super-resolution. Our architecture achieves Peak
signal-to-noise-ratio (PSNR) of 47.89, 49.00 and 45.32 on three public
datasets, namely iChallenge-AMD, iChallenge-PM, and G1020. Additionally, we
tested the model's effectiveness on a privately held dataset for SANS provided
by NASA and achieved comparable results against previous architectures.Comment: Accepted in 26th International Conference on Medical Image Computing
and Computer Assisted Intervention, MICCAI 202
Stroboscopic Augmented Reality as an Approach to Mitigate Gravitational Transition Effects During Interplanetary Spaceflight
During interplanetary spaceflight, periods of extreme gravitational transitions will occur such as transitions between hypergravity, hypogravity, and microgravity. Following gravitational transitions, rapid sensorimotor adaptation or maladaptation may occur which can affect gaze control and weaken dynamic visual acuity in astronauts. A reduction in dynamic visual acuity during spaceflight could possibly impact or impair mission critical activities (e.g., control of extraterrestrial machinery/vehicles and other important tasks). Stroboscopic visual training is an emerging visual tool that has been terrestrially observed to enhance visual performance and perception by performing tasks under conditions of intermittent vision. This technique has also been seen to increase the dynamic visual acuity for individuals terrestrially. To mitigate the decreased dynamic visual acuity that is observed in astronauts following gravitational transitions, stroboscopic vision training may serve as a potential countermeasure. We describe the effects of gravitational transitions on the vestibulo-ocular system and dynamic visual acuity, review terrestrial stroboscopic visual training, and report the novel development of stroboscopic augmented reality as a possible countermeasure for G-transitions in future spaceflight