72 research outputs found
Population Density-based Hospital Recommendation with Mobile LBS Big Data
The difficulty of getting medical treatment is one of major livelihood issues
in China. Since patients lack prior knowledge about the spatial distribution
and the capacity of hospitals, some hospitals have abnormally high or sporadic
population densities. This paper presents a new model for estimating the
spatiotemporal population density in each hospital based on location-based
service (LBS) big data, which would be beneficial to guiding and dispersing
outpatients. To improve the estimation accuracy, several approaches are
proposed to denoise the LBS data and classify people by detecting their various
behaviors. In addition, a long short-term memory (LSTM) based deep learning is
presented to predict the trend of population density. By using Baidu
large-scale LBS logs database, we apply the proposed model to 113 hospitals in
Beijing, P. R. China, and constructed an online hospital recommendation system
which can provide users with a hospital rank list basing the real-time
population density information and the hospitals' basic information such as
hospitals' levels and their distances. We also mine several interesting
patterns from these LBS logs by using our proposed system
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
To minimize the impact of age variation on face recognition, age-invariant
face recognition (AIFR) extracts identity-related discriminative features by
minimizing the correlation between identity- and age-related features while
face age synthesis (FAS) eliminates age variation by converting the faces in
different age groups to the same group. However, AIFR lacks visual results for
model interpretation and FAS compromises downstream recognition due to
artifacts. Therefore, we propose a unified, multi-task framework to jointly
handle these two tasks, termed MTLFace, which can learn the age-invariant
identity-related representation for face recognition while achieving pleasing
face synthesis for model interpretation. Specifically, we propose an
attention-based feature decomposition to decompose the mixed face features into
two uncorrelated components -- identity- and age-related features -- in a
spatially constrained way. Unlike the conventional one-hot encoding that
achieves group-level FAS, we propose a novel identity conditional module to
achieve identity-level FAS, which can improve the age smoothness of synthesized
faces through a weight-sharing strategy. Benefiting from the proposed
multi-task framework, we then leverage those high-quality synthesized faces
from FAS to further boost AIFR via a novel selective fine-tuning strategy.
Furthermore, to advance both AIFR and FAS, we collect and release a large
cross-age face dataset with age and gender annotations, and a new benchmark
specifically designed for tracing long-missing children. Extensive experimental
results on five benchmark cross-age datasets demonstrate that MTLFace yields
superior performance for both AIFR and FAS. We further validate MTLFace on two
popular general face recognition datasets, obtaining competitive performance on
face recognition in the wild. Code is available at
http://hzzone.github.io/MTLFace.Comment: TPAMI 2022. arXiv admin note: substantial text overlap with
arXiv:2103.0152
BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation
Medical image segmentation is a challenging task with inherent ambiguity and
high uncertainty, attributed to factors such as unclear tumor boundaries and
multiple plausible annotations. The accuracy and diversity of segmentation
masks are both crucial for providing valuable references to radiologists in
clinical practice. While existing diffusion models have shown strong capacities
in various visual generation tasks, it is still challenging to deal with
discrete masks in segmentation. To achieve accurate and diverse medical image
segmentation masks, we propose a novel conditional Bernoulli Diffusion model
for medical image segmentation (BerDiff). Instead of using the Gaussian noise,
we first propose to use the Bernoulli noise as the diffusion kernel to enhance
the capacity of the diffusion model for binary segmentation tasks, resulting in
more accurate segmentation masks. Second, by leveraging the stochastic nature
of the diffusion model, our BerDiff randomly samples the initial Bernoulli
noise and intermediate latent variables multiple times to produce a range of
diverse segmentation masks, which can highlight salient regions of interest
that can serve as valuable references for radiologists. In addition, our
BerDiff can efficiently sample sub-sequences from the overall trajectory of the
reverse diffusion, thereby speeding up the segmentation process. Extensive
experimental results on two medical image segmentation datasets with different
modalities demonstrate that our BerDiff outperforms other recently published
state-of-the-art methods. Our results suggest diffusion models could serve as a
strong backbone for medical image segmentation.Comment: 14 pages, 7 figure
Learning Representation for Clustering via Prototype Scattering and Positive Sampling
Existing deep clustering methods rely on either contrastive or
non-contrastive representation learning for downstream clustering task.
Contrastive-based methods thanks to negative pairs learn uniform
representations for clustering, in which negative pairs, however, may
inevitably lead to the class collision issue and consequently compromise the
clustering performance. Non-contrastive-based methods, on the other hand, avoid
class collision issue, but the resulting non-uniform representations may cause
the collapse of clustering. To enjoy the strengths of both worlds, this paper
presents a novel end-to-end deep clustering method with prototype scattering
and positive sampling, termed ProPos. Specifically, we first maximize the
distance between prototypical representations, named prototype scattering loss,
which improves the uniformity of representations. Second, we align one
augmented view of instance with the sampled neighbors of another view --
assumed to be truly positive pair in the embedding space -- to improve the
within-cluster compactness, termed positive sampling alignment. The strengths
of ProPos are avoidable class collision issue, uniform representations,
well-separated clusters, and within-cluster compactness. By optimizing ProPos
in an end-to-end expectation-maximization framework, extensive experimental
results demonstrate that ProPos achieves competing performance on
moderate-scale clustering benchmark datasets and establishes new
state-of-the-art performance on large-scale datasets. Source code is available
at \url{https://github.com/Hzzone/ProPos}.Comment: Accepted by TPAMI 202
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