48 research outputs found
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation
performance, data annotation is still a big bottleneck due to the
labor-intensive and time-consuming burden on medical experts, especially for 3D
images. To significantly reduce annotation efforts while attaining competitive
segmentation accuracy, we propose a self-learning and one-shot learning based
framework for 3D medical image segmentation by annotating only one slice of
each 3D image. Our approach takes two steps: (1) self-learning of a
reconstruction network to learn semantic correspondence among 2D slices within
3D images, and (2) representative selection of single slices for one-shot
manual annotation and propagating the annotated data with the well-trained
reconstruction network. Extensive experiments verify that our new framework
achieves comparable performance with less than 1% annotated data compared with
fully supervised methods and generalizes well on several out-of-distribution
testing sets
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels
Since annotating medical images for segmentation tasks commonly incurs
expensive costs, it is highly desirable to design an annotation-efficient
method to alleviate the annotation burden. Recently, contrastive learning has
exhibited a great potential in learning robust representations to boost
downstream tasks with limited labels. In medical imaging scenarios, ready-made
meta labels (i.e., specific attribute information of medical images) inherently
reveal semantic relationships among images, which have been used to define
positive pairs in previous work. However, the multi-perspective semantics
revealed by various meta labels are usually incompatible and can incur
intractable "semantic contradiction" when combining different meta labels. In
this paper, we tackle the issue of "semantic contradiction" in a
gradient-guided manner using our proposed Gradient Mitigator method, which
systematically unifies multi-perspective meta labels to enable a pre-trained
model to attain a better high-level semantic recognition ability. Moreover, we
emphasize that the fine-grained discrimination ability is vital for
segmentation-oriented pre-training, and develop a novel method called Gradient
Filter to dynamically screen pixel pairs with the most discriminating power
based on the magnitude of gradients. Comprehensive experiments on four medical
image segmentation datasets verify that our new method GCL: (1) learns
informative image representations and considerably boosts segmentation
performance with limited labels, and (2) shows promising generalizability on
out-of-distribution datasets
Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods
Bone age assessment is challenging in clinical practice due to the
complicated bone age assessment process. Current automatic bone age assessment
methods were designed with rare consideration of the diagnostic logistics and
thus may yield certain uninterpretable hidden states and outputs. Consequently,
doctors can find it hard to cooperate with such models harmoniously because it
is difficult to check the correctness of the model predictions. In this work,
we propose a new graph-based deep learning framework for bone age assessment
with hand radiographs, called Doctor Imitator (DI). The architecture of DI is
designed to learn the diagnostic logistics of doctors using the scoring methods
(e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the
convolutions of DI capture the local features of the anatomical regions of
interest (ROIs) on hand radiographs and predict the ROI scores by our proposed
Anatomy-based Group Convolution, summing up for bone age prediction. Besides,
we develop a novel Dual Graph-based Attention module to compute
patient-specific attention for ROI features and context attention for ROI
scores. As far as we know, DI is the first automatic bone age assessment
framework following the scoring methods without fully supervised hand
radiographs. Experiments on hand radiographs with only bone age supervision
verify that DI can achieve excellent performance with sparse parameters and
provide more interpretability.Comment: Original Title: "Doctor Imitator: A Graph-based Bone Age Assessment
Framework Using Hand Radiographs" @inproceedings{chen2020doctor,
title={Doctor imitator: A graph-based bone age assessment framework using
hand radiographs}, author={Chen, Jintai and Yu, Bohan and Lei, Biwen and
Feng, Ruiwei and Chen, Danny Z and Wu, Jian}, booktitle={MICCAI}, year={2020}
Clean air for some : Unintended spillover effects of regional air pollution policies
China has enacted a number of ambitious pollution control policies to mitigate air pollution in urban areas. Unintended side effects of these policies to other environmental policy arenas and regions have largely been ignored. To bridge this gap, we use a multiregional input-output model in combination with an atmospheric chemical transport model to simulate clean air policy scenarios and evaluate their environmental impacts on primary PM2.5 and secondary precursor emissions, as well as CO2 emissions and water consumption, in the target region and spillover effects to other regions. Our results show that the reduction in primary PM2.5 and secondary precursor emissions in the target regions comes at the cost of increasing emissions especially in neighboring provinces. Similarly, co-benefits of lower CO2 emissions and reduced water consumption in the target region are achieved at the expense of higher impacts elsewhere, through outsourcing production to less developed regions in China
PGformer: Proxy-Bridged Game Transformer for Multi-Person Extremely Interactive Motion Prediction
Multi-person motion prediction is a challenging task, especially for
real-world scenarios of densely interacted persons. Most previous works have
been devoted to studying the case of weak interactions (e.g., hand-shaking),
which typically forecast each human pose in isolation. In this paper, we focus
on motion prediction for multiple persons with extreme collaborations and
attempt to explore the relationships between the highly interactive persons'
motion trajectories. Specifically, a novel cross-query attention (XQA) module
is proposed to bilaterally learn the cross-dependencies between the two pose
sequences tailored for this situation. Additionally, we introduce and build a
proxy entity to bridge the involved persons, which cooperates with our proposed
XQA module and subtly controls the bidirectional information flows, acting as a
motion intermediary. We then adapt these designs to a Transformer-based
architecture and devise a simple yet effective end-to-end framework called
proxy-bridged game Transformer (PGformer) for multi-person interactive motion
prediction. The effectiveness of our method has been evaluated on the
challenging ExPI dataset, which involves highly interactive actions. We show
that our PGformer consistently outperforms the state-of-the-art methods in both
short- and long-term predictions by a large margin. Besides, our approach can
also be compatible with the weakly interacted CMU-Mocap and MuPoTS-3D datasets
and achieve encouraging results. Our code will become publicly available upon
acceptance
Odd-Char Multivariate Hidden Field Equations
We present a multivariate version of Hidden Field Equations (HFE)
over a finite field of odd characteristic, with an extra
``embedding\u27\u27 modifier. Combining these known ideas makes our new
MPKC (multivariate public key cryptosystem) more efficient
and scalable than any other extant multivariate encryption scheme.
Switching to odd characteristics in HFE-like schemes affects how an
attacker can make use of field equations. Extensive empirical tests
(using MAGMA-2.14, the best commercially available \mathbold{F_4}
implementation) suggests that our new construction is indeed secure
against algebraic attacks using Gröbner Basis algorithms. The
``embedding\u27\u27 serves both to narrow down choices of pre-images and
to guard against a possible Kipnis-Shamir type (rank-based) attack. We
may hence reasonably argue that for practical sizes, prior attacks
take exponential time.
We demonstrate that our construction is in fact efficient by
implementing practical-sized examples of our ``odd-char HFE\u27\u27 with 3
variables (``THFE\u27\u27) over . To be precise, our preliminary
THFE implementation is -- the speed of RSA-1024
Small Odd Prime Field Multivariate PKCs
We show that Multivariate Public Key Cryptosystems (MPKCs)
over fields of small odd prime characteristic, say 31, can be highly
efficient. Indeed, at the same design security of under
the best known attacks, odd-char MPKC is generally
faster than prior MPKCs over \GF{2^k}, which are in turn faster than
``traditional\u27\u27 alternatives.
This seemingly counter-intuitive feat is accomplished by exploiting
the comparative over-abundance of small integer arithmetic resources
in commodity hardware, here embodied by SSE2 or more advanced
special multimedia instructions on modern x86-compatible CPUs.
We explain our implementation techniques and design choices in
implementing our chosen MPKC instances modulo small a odd prime.
The same techniques are also applicable in modern FPGAs which often
contains a large number of multipliers