49,781 research outputs found
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR
2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4:
correction, update reference baseline results according to their latest post;
V5: minor correction; V6: Identification results using NIH data splits and
various image model
Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data
Creating large-scale and well-annotated datasets to train AI algorithms is
crucial for automated tumor detection and localization. However, with limited
resources, it is challenging to determine the best type of annotations when
annotating massive amounts of unlabeled data. To address this issue, we focus
on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans;
both applications require significant effort and time for pixel-wise annotation
due to the high dimensional nature of the data, involving either temporary or
spatial dimensions. In this paper, we develop a new annotation strategy, termed
Drag&Drop, which simplifies the annotation process to drag and drop. This
annotation strategy is more efficient, particularly for temporal and volumetric
imaging, than other types of weak annotations, such as per-pixel, bounding
boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop
annotations, we develop a novel weakly supervised learning method based on the
watershed algorithm. Experimental results show that our method achieves better
detection and localization performance than alternative weak annotations and,
more importantly, achieves similar performance to that trained on detailed
per-pixel annotations. Interestingly, we find that, with limited resources,
allocating weak annotations from a diverse patient population can foster models
more robust to unseen images than allocating per-pixel annotations for a small
set of images. In summary, this research proposes an efficient annotation
strategy for tumor detection and localization that is less accurate than
per-pixel annotations but useful for creating large-scale datasets for
screening tumors in various medical modalities.Comment: Published in Machine Intelligence Researc
Biases in the Experimental Annotations of Protein Function and their Effect on Our Understanding of Protein Function Space
The ongoing functional annotation of proteins relies upon the work of
curators to capture experimental findings from scientific literature and apply
them to protein sequence and structure data. However, with the increasing use
of high-throughput experimental assays, a small number of experimental studies
dominate the functional protein annotations collected in databases. Here we
investigate just how prevalent is the "few articles -- many proteins"
phenomenon. We examine the experimentally validated annotation of proteins
provided by several groups in the GO Consortium, and show that the distribution
of proteins per published study is exponential, with 0.14% of articles
providing the source of annotations for 25% of the proteins in the UniProt-GOA
compilation. Since each of the dominant articles describes the use of an assay
that can find only one function or a small group of functions, this leads to
substantial biases in what we know about the function of many proteins.
Mass-spectrometry, microscopy and RNAi experiments dominate high throughput
experiments. Consequently, the functional information derived from these
experiments is mostly of the subcellular location of proteins, and of the
participation of proteins in embryonic developmental pathways. For some
organisms, the information provided by different studies overlap by a large
amount. We also show that the information provided by high throughput
experiments is less specific than those provided by low throughput experiments.
Given the experimental techniques available, certain biases in protein function
annotation due to high-throughput experiments are unavoidable. Knowing that
these biases exist and understanding their characteristics and extent is
important for database curators, developers of function annotation programs,
and anyone who uses protein function annotation data to plan experiments.Comment: Accepted to PLoS Computational Biology. Press embargo applies. v4:
text corrected for style and supplementary material inserte
Localizing Actions from Video Labels and Pseudo-Annotations
The goal of this paper is to determine the spatio-temporal location of
actions in video. Where training from hard to obtain box annotations is the
norm, we propose an intuitive and effective algorithm that localizes actions
from their class label only. We are inspired by recent work showing that
unsupervised action proposals selected with human point-supervision perform as
well as using expensive box annotations. Rather than asking users to provide
point supervision, we propose fully automatic visual cues that replace manual
point annotations. We call the cues pseudo-annotations, introduce five of them,
and propose a correlation metric for automatically selecting and combining
them. Thorough evaluation on challenging action localization datasets shows
that we reach results comparable to results with full box supervision. We also
show that pseudo-annotations can be leveraged during testing to improve weakly-
and strongly-supervised localizers.Comment: BMV
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
This paper introduces a video dataset of spatio-temporally localized Atomic
Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual
actions in 430 15-minute video clips, where actions are localized in space and
time, resulting in 1.58M action labels with multiple labels per person
occurring frequently. The key characteristics of our dataset are: (1) the
definition of atomic visual actions, rather than composite actions; (2) precise
spatio-temporal annotations with possibly multiple annotations for each person;
(3) exhaustive annotation of these atomic actions over 15-minute video clips;
(4) people temporally linked across consecutive segments; and (5) using movies
to gather a varied set of action representations. This departs from existing
datasets for spatio-temporal action recognition, which typically provide sparse
annotations for composite actions in short video clips. We will release the
dataset publicly.
AVA, with its realistic scene and action complexity, exposes the intrinsic
difficulty of action recognition. To benchmark this, we present a novel
approach for action localization that builds upon the current state-of-the-art
methods, and demonstrates better performance on JHMDB and UCF101-24 categories.
While setting a new state of the art on existing datasets, the overall results
on AVA are low at 15.6% mAP, underscoring the need for developing new
approaches for video understanding.Comment: To appear in CVPR 2018. Check dataset page
https://research.google.com/ava/ for detail
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