1,800 research outputs found
Imperfect Construction of Microclusters
Microclusters are the basic building blocks used to construct cluster states
capable of supporting fault-tolerant quantum computation. In this paper, we
explore the consequences of errors on microcluster construction using two error
models. To quantify the effect of the errors we calculate the fidelity of the
constructed microclusters and the fidelity with which two such microclusters
can be fused together. Such simulations are vital for gauging the capability of
an experimental system to achieve fault tolerance.Comment: 5 pages 2 figure
Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets
There exists a large number of datasets for organ segmentation, which are
partially annotated, and sequentially constructed. A typical dataset is
constructed at a certain time by curating medical images and annotating the
organs of interest. In other words, new datasets with annotations of new organ
categories are built over time. To unleash the potential behind these partially
labeled, sequentially-constructed datasets, we propose to learn a multi-organ
segmentation model through incremental learning (IL). In each IL stage, we lose
access to the previous annotations, whose knowledge is assumingly captured by
the current model, and gain the access to a new dataset with annotations of new
organ categories, from which we learn to update the organ segmentation model to
include the new organs. We give the first attempt to conjecture that the
different distribution is the key reason for 'catastrophic forgetting' that
commonly exists in IL methods, and verify that IL has the natural adaptability
to medical image scenarios. Extensive experiments on five open-sourced datasets
are conducted to prove the effectiveness of our method and the conjecture
mentioned above
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many
clinical applications, such as pathological diagnosis of hepatic diseases,
surgical planning, and postoperative assessment. However, it is still a very
challenging task due to the complex background, fuzzy boundary, and various
appearance of liver. In this paper, we propose an automatic and efficient
algorithm to segment liver from 3D CT volumes. A deep image-to-image network
(DI2IN) is first deployed to generate the liver segmentation, employing a
convolutional encoder-decoder architecture combined with multi-level feature
concatenation and deep supervision. Then an adversarial network is utilized
during training process to discriminate the output of DI2IN from ground truth,
which further boosts the performance of DI2IN. The proposed method is trained
on an annotated dataset of 1000 CT volumes with various different scanning
protocols (e.g., contrast and non-contrast, various resolution and position)
and large variations in populations (e.g., ages and pathology). Our approach
outperforms the state-of-the-art solutions in terms of segmentation accuracy
and computing efficiency.Comment: Accepted by MICCAI 201
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