61 research outputs found
Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction
Deep learning (DL) reconstruction particularly of MRI has led to improvements
in image fidelity and reduction of acquisition time. In neuroimaging, DL
methods can reconstruct high-quality images from undersampled data. However, it
is essential to consider fairness in DL algorithms, particularly in terms of
demographic characteristics. This study presents the first fairness analysis in
a DL-based brain MRI reconstruction model. The model utilises the U-Net
architecture for image reconstruction and explores the presence and sources of
unfairness by implementing baseline Empirical Risk Minimisation (ERM) and
rebalancing strategies. Model performance is evaluated using image
reconstruction metrics. Our findings reveal statistically significant
performance biases between the gender and age subgroups. Surprisingly, data
imbalance and training discrimination are not the main sources of bias. This
analysis provides insights of fairness in DL-based image reconstruction and
aims to improve equity in medical AI applications.Comment: Accepted for publication at FAIMI 2023 (Fairness of AI in Medical
Imaging) at MICCA
Context Perception Parallel Decoder for Scene Text Recognition
Scene text recognition (STR) methods have struggled to attain high accuracy
and fast inference speed. Autoregressive (AR)-based STR model uses the
previously recognized characters to decode the next character iteratively. It
shows superiority in terms of accuracy. However, the inference speed is slow
also due to this iteration. Alternatively, parallel decoding (PD)-based STR
model infers all the characters in a single decoding pass. It has advantages in
terms of inference speed but worse accuracy, as it is difficult to build a
robust recognition context in such a pass. In this paper, we first present an
empirical study of AR decoding in STR. In addition to constructing a new AR
model with the top accuracy, we find out that the success of AR decoder lies
also in providing guidance on visual context perception rather than language
modeling as claimed in existing studies. As a consequence, we propose Context
Perception Parallel Decoder (CPPD) to decode the character sequence in a single
PD pass. CPPD devises a character counting module and a character ordering
module. Given a text instance, the former infers the occurrence count of each
character, while the latter deduces the character reading order and
placeholders. Together with the character prediction task, they construct a
context that robustly tells what the character sequence is and where the
characters appear, well mimicking the context conveyed by AR decoding.
Experiments on both English and Chinese benchmarks demonstrate that CPPD models
achieve highly competitive accuracy. Moreover, they run approximately 7x faster
than their AR counterparts, and are also among the fastest recognizers. The
code will be released soon
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's
function and condition in a non-invasive manner. Undersampling of the -space
is employed to reduce the scan duration, thus increasing patient comfort and
reducing the risk of motion artefacts, at the cost of reduced image quality. In
this challenge paper, we investigate the use of a convolutional recurrent
neural network (CRNN) architecture to exploit temporal correlations in
supervised cine cardiac MRI reconstruction. This is combined with a
single-image super-resolution refinement module to improve single coil
reconstruction by 4.4\% in structural similarity and 3.9\% in normalised mean
square error compared to a plain CRNN implementation. We deploy a high-pass
filter to our loss to allow greater emphasis on high-frequency details
which are missing in the original data. The proposed model demonstrates
considerable enhancements compared to the baseline case and holds promising
potential for further improving cardiac MRI reconstruction.Comment: MICCAI STACOM workshop 202
Underload city conceptual approach extending ghost city studies
Global population growth and land development are highly imbalanced, marked by 43% of population increase but 150% of builtup area expansion from 1990 to 2018. This results in the widely concerned ghost city phenomenon and runs against the sustainable development goals. Existing studies identify ghost cities by population densities, but ignore the spatial heterogeneity of land carrying capacities (LCC). Accordingly, this study proposes a general concept termed underload city to define cities carrying fewer people and lower economic strength than their LCC. The underload city essentially describes imbalanced human-land relationship and is understood in a broader context than the usually applied ghost city. In this study, very high-resolution satellite images are
analyzed to obtain land functional structures, and further combined with population and GDP data to derive LCC. We empirically identify eight underload cities among 81 major Chinese cities, differing from previous findings of ghost cities. Accordingly, the proposed underload city considers heterogeneous human-land relationships when assessing city loads and contributes to sustainable city developments
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