31 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 End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
Anatomical landmark correspondences in medical images can provide additional
guidance information for the alignment of two images, which, in turn, is
crucial for many medical applications. However, manual landmark annotation is
labor-intensive. Therefore, we propose an end-to-end deep learning approach to
automatically detect landmark correspondences in pairs of two-dimensional (2D)
images. Our approach consists of a Siamese neural network, which is trained to
identify salient locations in images as landmarks and predict matching
probabilities for landmark pairs from two different images. We trained our
approach on 2D transverse slices from 168 lower abdominal Computed Tomography
(CT) scans. We tested the approach on 22,206 pairs of 2D slices with varying
levels of intensity, affine, and elastic transformations. The proposed approach
finds an average of 639, 466, and 370 landmark matches per image pair for
intensity, affine, and elastic transformations, respectively, with spatial
matching errors of at most 1 mm. Further, more than 99% of the landmark pairs
are within a spatial matching error of 2 mm, 4 mm, and 8 mm for image pairs
with intensity, affine, and elastic transformations, respectively. To
investigate the utility of our developed approach in a clinical setting, we
also tested our approach on pairs of transverse slices selected from follow-up
CT scans of three patients. Visual inspection of the results revealed landmark
matches in both bony anatomical regions as well as in soft tissues lacking
prominent intensity gradients.Comment: SPIE Medical Imaging Conference - 202
The case of a pregnant woman with ARDS due to COVID-19 treated with Hydroxychloroquine, Azithromycin, and Remdesivir and delivery of a healthy baby during mechanical ventilation through cesarean section
We are in the midst of a pandemic due to SARS-CoV-2. Pregnancy was identified among the risk factors for worse clinical outcomes in multiple studies. The optimal therapy in this group of patients remains to be defined. Here we present the case of a 39 years old Caucasian pregnant female at 31 weeks of gestation who was treated successfully with hydroxychloroquine, azithromycin, remdesivir, prone therapy, and cesarean delivery of a healthy baby while on mechanical ventilation. The SARS-CoV-2 remained positive until the 39th day of hospitalization. We discussed the pathophysiology of the increase risk of infections during pregnancy and particularly the high risk of microthombosis and coagulopathy due to COVID-19. Ultimately the contribution of the medications used to the favorable outcomes remained unknown being more likely that the delivery helped resolve the infection
Mixed-block neural architecture search for medical image segmentation
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task-specific. Neural Architecture Search (NAS), i.e., the automation of neural network design, has been shown to have the capability to outperform manually designed networks for various tasks. However, the existing NAS methods for medical image segmentation have explored a quite limited range of types of DNN architectures that can be discovered. In this work, we propose a novel NAS search space for medical image segmentation networks. This search space combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have proven to have a strong performance in image classification tasks. The search is performed by looking for the best topology of multiple cells simultaneously with the configuration of each cell within, allowing for interactions between topology and cell-level attributes. From experiments on two publicly available datasets, we find that the networks discovered by our proposed NAS method have better performance than well-known handcrafted segmentation networks, and outperform networks found with other NAS approaches that perform only topology search, and topology-level search followed by cell-level search
An end-to-end deep learning approach for landmark detection and matching in medical images
Anatomical landmark correspondences in medical images can provide additional guidance information for the alignment of two images, which, in turn, is crucial for many medical applications. However, manual landmark annotation is labor-intensive. Therefore, we propose an end-to-end deep learning approach to automatically detect landmark correspondences in pairs of two-dimensional (2D) images. Our approach consists of a Siamese neural network, which is trained to identify salient locations in images as landmarks and predict matching probabilities for landmark pairs from two different images. We trained our approach on 2D transverse slices from 168 lower abdominal Computed Tomography (CT) scans. We tested the approach on 22,206 pairs of 2D slices with varying levels of intensity, affine, and elastic transformations. The proposed approach finds an average of 639, 466, and 370 landmark matches per image pair for intensity, affine, and elastic transformations, respectively, with spatial matching errors of at most 1 mm. Further, more than 99% of the landmark pairs are within a spatial
Clinically acceptable segmentation of Organs at Risk in cervical cancer radiation treatment from clinically available annotations
Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student setup, annotation imputation, and uncertainty-guided training to learn in presence of missing annotations. Our experimental results show that learning from a large dataset with our approach yields a significant improvement in the test performance despite missing annotations in the data. Further, the contours generated from the segmentation masks predicted by our model are found to be equally clinically acceptable as manually generated contours
monikagrewal /End2EndLandmarks
Code for the paper An end-to-end deep learning approach for landmark detection and matching in medical images. The full text of the paper is available at https://arxiv.org/pdf/2001.07434v1.pdf
Comparison of consumer reactions to price-matching guarantees in internet and bricks-and-mortar retail environments
Abstract The present study investigates consumer responses to price-matching guarantees (PMGs) in the Internet environment and contrasts them with their responses in a traditional bricks-and-mortar retail environment. The effect of store reputation on consumer responses to price-matching policies is also investigated in both Internet and bricks-andmortar retail settings. Two studies using a 2×2×2 betweensubjects full factorial experimental design with two levels of PMG presence (PMG present, PMG absent), two levels of retail environment (Internet, bricks-and-mortar), and two levels of store reputation (no/low reputation, high reputation) were conducted. In study 1 reputation was manipulated using store names, while in study 2 the reputation was manipulated using store characteristics. The findings of two studies suggest that consumer reactions to price-matching guarantees, such as store price perceptions, postpurchase search intentions, and willingness to claim a refund if a lower competitive price is found, differ across the two purchase environments