12,755 research outputs found
PadChest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain) from 2009 to 2017, covering six different position views and
additional information on image acquisition and patient demography. The reports
were labeled with 174 different radiographic findings, 19 differential
diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and
mapped onto standard Unified Medical Language System (UMLS) terminology. Of
these reports, 27% were manually annotated by trained physicians and the
remaining set was labeled using a supervised method based on a recurrent neural
network with attention mechanisms. The labels generated were then validated in
an independent test set achieving a 0.93 Micro-F1 score. To the best of our
knowledge, this is one of the largest public chest x-ray database suitable for
training supervised models concerning radiographs, and the first to contain
radiographic reports in Spanish. The PadChest dataset can be downloaded from
http://bimcv.cipf.es/bimcv-projects/padchest/
Towards Universal Semantic Tagging
The paper proposes the task of universal semantic tagging---tagging word
tokens with language-neutral, semantically informative tags. We argue that the
task, with its independent nature, contributes to better semantic analysis for
wide-coverage multilingual text. We present the initial version of the semantic
tagset and show that (a) the tags provide semantically fine-grained
information, and (b) they are suitable for cross-lingual semantic parsing. An
application of the semantic tagging in the Parallel Meaning Bank supports both
of these points as the tags contribute to formal lexical semantics and their
cross-lingual projection. As a part of the application, we annotate a small
corpus with the semantic tags and present new baseline result for universal
semantic tagging.Comment: 9 pages, International Conference on Computational Semantics (IWCS
Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection
There is a need for affordable, widely deployable maternal-fetal ECG monitors
to improve maternal and fetal health during pregnancy and delivery. Based on
the diffusion-based channel selection, here we present the mathematical
formalism and clinical validation of an algorithm capable of accurate
separation of maternal and fetal ECG from a two channel signal acquired over
maternal abdomen
Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images
We address the problem of fine-grained action localization from temporally
untrimmed web videos. We assume that only weak video-level annotations are
available for training. The goal is to use these weak labels to identify
temporal segments corresponding to the actions, and learn models that
generalize to unconstrained web videos. We find that web images queried by
action names serve as well-localized highlights for many actions, but are
noisily labeled. To solve this problem, we propose a simple yet effective
method that takes weak video labels and noisy image labels as input, and
generates localized action frames as output. This is achieved by cross-domain
transfer between video frames and web images, using pre-trained deep
convolutional neural networks. We then use the localized action frames to train
action recognition models with long short-term memory networks. We collect a
fine-grained sports action data set FGA-240 of more than 130,000 YouTube
videos. It has 240 fine-grained actions under 85 sports activities. Convincing
results are shown on the FGA-240 data set, as well as the THUMOS 2014
localization data set with untrimmed training videos.Comment: Camera ready version for ACM Multimedia 201
DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
Despite recent progress on computer vision and natural language processing,
developing video understanding intelligence is still hard to achieve due to the
intrinsic difficulty of story in video. Moreover, there is not a theoretical
metric for evaluating the degree of video understanding. In this paper, we
propose a novel video question answering (Video QA) task, DramaQA, for a
comprehensive understanding of the video story. The DramaQA focused on two
perspectives: 1) hierarchical QAs as an evaluation metric based on the
cognitive developmental stages of human intelligence. 2) character-centered
video annotations to model local coherence of the story. Our dataset is built
upon the TV drama "Another Miss Oh" and it contains 16,191 QA pairs from 23,928
various length video clips, with each QA pair belonging to one of four
difficulty levels. We provide 217,308 annotated images with rich
character-centered annotations, including visual bounding boxes, behaviors, and
emotions of main characters, and coreference resolved scripts. Additionally, we
provide analyses of the dataset as well as Dual Matching Multistream model
which effectively learns character-centered representations of video to answer
questions about the video. We are planning to release our dataset and model
publicly for research purposes and expect that our work will provide a new
perspective on video story understanding research.Comment: 21 pages, 10 figures, submitted to ECCV 202
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