20 research outputs found
FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging
Inverse problems are essential to imaging applications. In this paper, we
propose a model-based deep learning network, named FISTA-Net, by combining the
merits of interpretability and generality of the model-based Fast Iterative
Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and
tuning-free advantages of the data-driven neural network. By unfolding the
FISTA into a deep network, the architecture of FISTA-Net consists of multiple
gradient descent, proximal mapping, and momentum modules in cascade. Different
from FISTA, the gradient matrix in FISTA-Net can be updated during iteration
and a proximal operator network is developed for nonlinear thresholding which
can be learned through end-to-end training. Key parameters of FISTA-Net
including the gradient step size, thresholding value and momentum scalar are
tuning-free and learned from training data rather than hand-crafted. We further
impose positive and monotonous constraints on these parameters to ensure they
converge properly. The experimental results, evaluated both visually and
quantitatively, show that the FISTA-Net can optimize parameters for different
imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational
Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep
learning methods and exhibits good generalization ability over other
competitive learning-based approaches under different noise levels.Comment: 11 pages
Image Reconstruction for Multi-frequency Electromagnetic Tomography based on Multiple Measurement Vector Model
Imaging the bio-impedance distribution of a biological sample can provide
understandings about the sample's electrical properties which is an important
indicator of physiological status. This paper presents a multi-frequency
electromagnetic tomography (mfEMT) technique for biomedical imaging. The system
consists of 8 channels of gradiometer coils with adjustable sensitivity and
excitation frequency. To exploit the frequency correlation among each
measurement, we reconstruct multiple frequency data simultaneously based on the
Multiple Measurement Vector (MMV) model. The MMV problem is solved by using a
sparse Bayesian learning method that is especially effective for sparse
distribution. Both simulations and experiments have been conducted to verify
the performance of the method. Results show that by taking advantage of
multiple measurements, the proposed method is more robust to noisy data for
ill-posed problems compared to the commonly used single measurement vector
model.Comment: This is an accepted paper which has been submitted to I2MTC 2020 on
Nov. 201
MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
The latest advancements in neural image compression show great potential in
surpassing the rate-distortion performance of conventional standard codecs.
Nevertheless, there exists an indelible domain gap between the datasets
utilized for training (i.e., natural images) and those utilized for inference
(e.g., artistic images). Our proposal involves a low-rank adaptation approach
aimed at addressing the rate-distortion drop observed in out-of-domain
datasets. Specifically, we perform low-rank matrix decomposition to update
certain adaptation parameters of the client's decoder. These updated
parameters, along with image latents, are encoded into a bitstream and
transmitted to the decoder in practical scenarios. Due to the low-rank
constraint imposed on the adaptation parameters, the resulting bit rate
overhead is small. Furthermore, the bit rate allocation of low-rank adaptation
is \emph{non-trivial}, considering the diverse inputs require varying
adaptation bitstreams. We thus introduce a dynamic gating network on top of the
low-rank adaptation method, in order to decide which decoder layer should
employ adaptation. The dynamic adaptation network is optimized end-to-end using
rate-distortion loss. Our proposed method exhibits universality across diverse
image datasets. Extensive results demonstrate that this paradigm significantly
mitigates the domain gap, surpassing non-adaptive methods with an average
BD-rate improvement of approximately across out-of-domain images.
Furthermore, it outperforms the most advanced instance adaptive methods by
roughly BD-rate. Ablation studies confirm our method's ability to
universally enhance various image compression architectures.Comment: Accepted by ACM MM 2023, 13 pages, 12 figure
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
The state-of-the-art neural video codecs have outperformed the most
sophisticated traditional codecs in terms of RD performance in certain cases.
However, utilizing them for practical applications is still challenging for two
major reasons. 1) Cross-platform computational errors resulting from floating
point operations can lead to inaccurate decoding of the bitstream. 2) The high
computational complexity of the encoding and decoding process poses a challenge
in achieving real-time performance. In this paper, we propose a real-time
cross-platform neural video codec, which is capable of efficiently decoding of
720P video bitstream from other encoding platforms on a consumer-grade GPU.
First, to solve the problem of inconsistency of codec caused by the uncertainty
of floating point calculations across platforms, we design a calibration
transmitting system to guarantee the consistent quantization of entropy
parameters between the encoding and decoding stages. The parameters that may
have transboundary quantization between encoding and decoding are identified in
the encoding stage, and their coordinates will be delivered by auxiliary
transmitted bitstream. By doing so, these inconsistent parameters can be
processed properly in the decoding stage. Furthermore, to reduce the bitrate of
the auxiliary bitstream, we rectify the distribution of entropy parameters
using a piecewise Gaussian constraint. Second, to match the computational
limitations on the decoding side for real-time video codec, we design a
lightweight model. A series of efficiency techniques enable our model to
achieve 25 FPS decoding speed on NVIDIA RTX 2080 GPU. Experimental results
demonstrate that our model can achieve real-time decoding of 720P videos while
encoding on another platform. Furthermore, the real-time model brings up to a
maximum of 24.2\% BD-rate improvement from the perspective of PSNR with the
anchor H.265.Comment: 14 page
Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction
Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in
helping us further understand the relationship between histology and patient
outcome. To drive innovation in this area, we setup a community-wide challenge
using the largest available dataset of its kind to assess nuclear segmentation
and cellular composition. Our challenge, named CoNIC, stimulated the
development of reproducible algorithms for cellular recognition with real-time
result inspection on public leaderboards. We conducted an extensive
post-challenge analysis based on the top-performing models using 1,658
whole-slide images of colon tissue. With around 700 million detected nuclei per
model, associated features were used for dysplasia grading and survival
analysis, where we demonstrated that the challenge's improvement over the
previous state-of-the-art led to significant boosts in downstream performance.
Our findings also suggest that eosinophils and neutrophils play an important
role in the tumour microevironment. We release challenge models and WSI-level
results to foster the development of further methods for biomarker discovery
Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes
A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way