2,023 research outputs found
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Optical coherence tomography (OCT) has become the most important imaging
modality in ophthalmology. A substantial amount of research has recently been
devoted to the development of machine learning (ML) models for the
identification and quantification of pathological features in OCT images. Among
the several sources of variability the ML models have to deal with, a major
factor is the acquisition device, which can limit the ML model's
generalizability. In this paper, we propose to reduce the image variability
across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an
unsupervised unpaired image transformation algorithm. The usefulness of this
approach is evaluated in the setting of retinal fluid segmentation, namely
intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a
segmentation model on images acquired with a source OCT device. Then we
evaluate the model on (1) source, (2) target and (3) transformed versions of
the target OCT images. The presented transformation strategy shows an F1 score
of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional
transformation approaches, this means an F1 score gain of 0.2 (0.12).Comment: * Contributed equally (order was defined by flipping a coin)
--------------- Accepted for publication in the "IEEE International Symposium
on Biomedical Imaging (ISBI) 2019
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
In this work, we propose a novel approach for generating videos of the six
basic facial expressions given a neutral face image. We propose to exploit the
face geometry by modeling the facial landmarks motion as curves encoded as
points on a hypersphere. By proposing a conditional version of manifold-valued
Wasserstein generative adversarial network (GAN) for motion generation on the
hypersphere, we learn the distribution of facial expression dynamics of
different classes, from which we synthesize new facial expression motions. The
resulting motions can be transformed to sequences of landmarks and then to
images sequences by editing the texture information using another conditional
Generative Adversarial Network. To the best of our knowledge, this is the first
work that explores manifold-valued representations with GAN to address the
problem of dynamic facial expression generation. We evaluate our proposed
approach both quantitatively and qualitatively on two public datasets;
Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the
effectiveness of our approach in generating realistic videos with continuous
motion, realistic appearance and identity preservation. We also show the
efficiency of our framework for dynamic facial expressions generation, dynamic
facial expression transfer and data augmentation for training improved emotion
recognition models
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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works
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