80 research outputs found
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Understanding people's actions and interactions typically depends on seeing
them. Automating the process of action recognition from visual data has been
the topic of much research in the computer vision community. But what if it is
too dark, or if the person is occluded or behind a wall? In this paper, we
introduce a neural network model that can detect human actions through walls
and occlusions, and in poor lighting conditions. Our model takes radio
frequency (RF) signals as input, generates 3D human skeletons as an
intermediate representation, and recognizes actions and interactions of
multiple people over time. By translating the input to an intermediate
skeleton-based representation, our model can learn from both vision-based and
RF-based datasets, and allow the two tasks to help each other. We show that our
model achieves comparable accuracy to vision-based action recognition systems
in visible scenarios, yet continues to work accurately when people are not
visible, hence addressing scenarios that are beyond the limit of today's
vision-based action recognition.Comment: ICCV 2019. The first two authors contributed equally to this pape
Sample-Specific Debiasing for Better Image-Text Models
Self-supervised representation learning on image-text data facilitates
crucial medical applications, such as image classification, visual grounding,
and cross-modal retrieval. One common approach involves contrasting
semantically similar (positive) and dissimilar (negative) pairs of data points.
Drawing negative samples uniformly from the training data set introduces false
negatives, i.e., samples that are treated as dissimilar but belong to the same
class. In healthcare data, the underlying class distribution is nonuniform,
implying that false negatives occur at a highly variable rate. To improve the
quality of learned representations, we develop a novel approach that corrects
for false negatives. Our method can be viewed as a variant of debiased
constrastive learning that uses estimated sample-specific class probabilities.
We provide theoretical analysis of the objective function and demonstrate the
proposed approach on both image and paired image-text data sets. Our
experiments demonstrate empirical advantages of sample-specific debiasing
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
We present a method for fast biomedical image atlas construction using neural
fields. Atlases are key to biomedical image analysis tasks, yet conventional
and deep network estimation methods remain time-intensive. In this preliminary
work, we frame subject-specific atlas building as learning a neural field of
deformable spatiotemporal observations. We apply our method to learning
subject-specific atlases and motion stabilization of dynamic BOLD MRI
time-series of fetuses in utero. Our method yields high-quality atlases of
fetal BOLD time-series with 5-7 faster convergence compared to
existing work. While our method slightly underperforms well-tuned baselines in
terms of anatomical overlap, it estimates templates significantly faster, thus
enabling rapid processing and stabilization of large databases of 4D dynamic
MRI acquisitions. Code is available at
https://github.com/Kidrauh/neural-atlasingComment: 6 pages, 2 figures. Accepted by Medical Imaging Meets NeurIPS 202
Evidence for an oncogenic role of HOXC6 in human non-small cell lung cancer
Background Identification of specific biomarkers is important for the diagnosis and treatment of non-small cell lung cancer (NSCLC). HOXC6 is a homeodomain-containing transcription factor that is highly expressed in several human cancers; however, its role in NSCLC remains unknown. Methods The expression and protein levels of HOXC6 were assessed in NSCLC tissue samples by Quantitative real-time PCR (qRT-PCR) and immunohistochemistry, respectively. HOXC6 was transfected into the NSCLC cell lines A549 and PC9, and used to investigate its effect on proliferation, migration, and invasion using CFSE, wound healing, and Matrigel invasion assays. Next-generation sequencing was also used to identify downstream targets of HOXC6 and to gain insights into the molecular mechanisms underlying its biological function. Results HOXC6 expression was significantly increased in 66.6% (20/30) of NSCLC tumor samples in comparison to normal controls. HOXC6 promoted proliferation, migration, and invasion of NSCLC cells in vitro. RNA-seq analysis demonstrated the upregulation of 310 and 112 genes in A549-HOXC6 and PC9-HOXC6 cells, respectively, and the downregulation of 665 and 385 genes in A549-HOXC6 and PC9-HOXC6 cells, respectively. HOXC6 was also found to regulate the expression of genes such as CEACAM6, SPARC, WNT6, CST1, MMP2, and KRT13, which have documented pro-tumorigenic functions. Discussion HOXC6 is highly expressed in NSCLC, and it may enhance lung cancer progression by regulating the expression of pro-tumorigenic genes involved in proliferation, migration, and invasion. Our study highlighted the oncogenic potential of HOXC6, and suggests that it may be a novel biomarker for the diagnosis and treatment of NSCLC
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