38 research outputs found
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we
experiment a set of deep learning models and present a cascaded deep neural
network that can diagnose all 14 pathologies better than the baseline and is
competitive with other published methods. Our work provides the quantitative
results to answer following research questions for the dataset: 1) What loss
functions to use for training DCNN from scratch on ChestX-ray14 dataset that
demonstrates high class imbalance and label co occurrence? 2) How to use
cascading to model label dependency and to improve accuracy of the deep
learning model?Comment: Submitted to CVPR 201
RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans
We describe a deep learning approach for automated brain hemorrhage detection
from computed tomography (CT) scans. Our model emulates the procedure followed
by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists,
the model sifts through 2D cross-sectional slices while paying close attention
to potential hemorrhagic regions. Further, the model utilizes 3D context from
neighboring slices to improve predictions at each slice and subsequently,
aggregates the slice-level predictions to provide diagnosis at CT level. We
refer to our proposed approach as Recurrent Attention DenseNet (RADnet) as it
employs original DenseNet architecture along with adding the components of
attention for slice level predictions and recurrent neural network layer for
incorporating 3D context. The real-world performance of RADnet has been
benchmarked against independent analysis performed by three senior radiologists
for 77 brain CTs. RADnet demonstrates 81.82% hemorrhage prediction accuracy at
CT level that is comparable to radiologists. Further, RADnet achieves higher
recall than two of the three radiologists, which is remarkable.Comment: Accepted at IEEE Symposium on Biomedical Imaging (ISBI) 2018 as
conference pape
An Overview of Stress-Strain Analysis for Elasticity Equations
The present chapter contains the analysis of stress, analysis of strain and stress-strain relationship through particular sections. The theory of elasticity contains equilibrium equations relating to stresses, kinematic equations relating to the strains and displacements and the constitutive equations relating to the stresses and strains. Concept of normal and shear stresses, principal stress, plane stress, Mohr’s circle, stress invariants and stress equilibrium relations are discussed in analysis of stress section while strain-displacement relationship for normal and shear strain, compatibility of strains are discussed in analysis of strain section through geometrical representations. Linear elasticity, generalized Hooke’s law and stress-strain relations for triclinic, monoclinic, orthotropic, transversely isotropic, fiber-reinforced and isotropic materials with some important relations for elasticity are discussed
Visual Dexterity: In-hand Dexterous Manipulation from Depth
In-hand object reorientation is necessary for performing many dexterous
manipulation tasks, such as tool use in unstructured environments that remain
beyond the reach of current robots. Prior works built reorientation systems
that assume one or many of the following specific circumstances: reorienting
only specific objects with simple shapes, limited range of reorientation, slow
or quasistatic manipulation, the need for specialized and costly sensor suites,
simulation-only results, and other constraints which make the system infeasible
for real-world deployment. We overcome these limitations and present a general
object reorientation controller that is trained using reinforcement learning in
simulation and evaluated in the real world. Our system uses readings from a
single commodity depth camera to dynamically reorient complex objects by any
amount in real time. The controller generalizes to novel objects not used
during training. It is successful in the most challenging test: the ability to
reorient objects in the air held by a downward-facing hand that must counteract
gravity during reorientation. The results demonstrate that the policy transfer
from simulation to the real world can be accomplished even for dynamic and
contact-rich tasks. Lastly, our hardware only uses open-source components that
cost less than five thousand dollars. Such construction makes it possible to
replicate the work and democratize future research in dexterous manipulation.
Videos are available at:
https://taochenshh.github.io/projects/visual-dexterity
Impact of comorbidity on patients with COVID-19 in India: A nationwide analysis
BackgroundThe emergence of coronavirus disease (COVID-19) as a global pandemic has resulted in the loss of many lives and a significant decline in global economic losses. Thus, for a large country like India, there is a need to comprehend the dynamics of COVID-19 in a clustered way.ObjectiveTo evaluate the clinical characteristics of patients with COVID-19 according to age, gender, and preexisting comorbidity. Patients with COVID-19 were categorized according to comorbidity, and the data over a 2-year period (1 January 2020 to 31 January 2022) were considered to analyze the impact of comorbidity on severe COVID-19 outcomes.MethodsFor different age/gender groups, the distribution of COVID-19 positive, hospitalized, and mortality cases was estimated. The impact of comorbidity was assessed by computing incidence rate (IR), odds ratio (OR), and proportion analysis.ResultsThe results indicated that COVID-19 caused an exponential growth in mortality. In patients over the age of 50, the mortality rate was found to be very high, ~80%. Moreover, based on the estimation of OR, it can be inferred that age and various preexisting comorbidities were found to be predictors of severe COVID-19 outcomes. The strongest risk factors for COVID-19 mortality were preexisting comorbidities like diabetes (OR: 2.39; 95% confidence interval (CI): 2.31–2.47; p < 0.0001), hypertension (OR: 2.31; 95% CI: 2.23–2.39; p < 0.0001), and heart disease (OR: 2.19; 95% CI: 2.08–2.30; p < 0.0001). The proportion of fatal cases among patients positive for COVID-19 increased with the number of comorbidities.ConclusionThis study concluded that elderly patients with preexisting comorbidities were at an increased risk of COVID-19 mortality. Patients in the elderly age group with underlying medical conditions are recommended for preventive medical care or medical resources and vaccination against COVID-19