17,586 research outputs found
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration combined
with human supervision is an emerging trend for many new surgical robot
platforms. One such task is automated anastomosis, which requires bimanual
needle handling and suture detection. Due to the complexity of the surgical
environment and varying patient anatomies, reliable suture detection is
difficult, which is further complicated by occlusion and thread topologies. In
this paper, we propose a multi-stage framework for suture thread detection
based on deep learning. Fully convolutional neural networks are used to obtain
the initial detection and the overlapping status of suture thread, which are
later fused with the original image to learn a gradient road map of the thread.
Based on the gradient road map, multiple segments of the thread are extracted
and linked to form the whole thread using a curvilinear structure detector.
Experiments on two different types of sutures demonstrate the accuracy of the
proposed framework.Comment: Submitted to ICRA 201
Lung Segmentation from Chest X-rays using Variational Data Imputation
Pulmonary opacification is the inflammation in the lungs caused by many
respiratory ailments, including the novel corona virus disease 2019 (COVID-19).
Chest X-rays (CXRs) with such opacifications render regions of lungs
imperceptible, making it difficult to perform automated image analysis on them.
In this work, we focus on segmenting lungs from such abnormal CXRs as part of a
pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the
high opacity regions as missing data and present a modified CNN-based image
segmentation network that utilizes a deep generative model for data imputation.
We train this model on normal CXRs with extensive data augmentation and
demonstrate the usefulness of this model to extend to cases with extreme
abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning
with Missing Values (Artemiss) hosted by the 37th International Conference on
Machine Learning (ICML). Source code, training data and the trained models
are available here: https://github.com/raghavian/lungVAE
Improving Missing Data Imputation with Deep Generative Models
Datasets with missing values are very common on industry applications, and
they can have a negative impact on machine learning models. Recent studies
introduced solutions to the problem of imputing missing values based on deep
generative models. Previous experiments with Generative Adversarial Networks
and Variational Autoencoders showed interesting results in this domain, but it
is not clear which method is preferable for different use cases. The goal of
this work is twofold: we present a comparison between missing data imputation
solutions based on deep generative models, and we propose improvements over
those methodologies. We run our experiments using known real life datasets with
different characteristics, removing values at random and reconstructing them
with several imputation techniques. Our results show that the presence or
absence of categorical variables can alter the selection of the best model, and
that some models are more stable than others after similar runs with different
random number generator seeds
- …