56 research outputs found
Channel Attention Separable Convolution Network for Skin Lesion Segmentation
Skin cancer is a frequently occurring cancer in the human population, and it
is very important to be able to diagnose malignant tumors in the body early.
Lesion segmentation is crucial for monitoring the morphological changes of skin
lesions, extracting features to localize and identify diseases to assist
doctors in early diagnosis. Manual de-segmentation of dermoscopic images is
error-prone and time-consuming, thus there is a pressing demand for precise and
automated segmentation algorithms. Inspired by advanced mechanisms such as
U-Net, DenseNet, Separable Convolution, Channel Attention, and Atrous Spatial
Pyramid Pooling (ASPP), we propose a novel network called Channel Attention
Separable Convolution Network (CASCN) for skin lesions segmentation. The
proposed CASCN is evaluated on the PH2 dataset with limited images. Without
excessive pre-/post-processing of images, CASCN achieves state-of-the-art
performance on the PH2 dataset with Dice similarity coefficient of 0.9461 and
accuracy of 0.9645.Comment: Accepted by ICONIP 202
Self-Attention Transducers for End-to-End Speech Recognition
Recurrent neural network transducers (RNN-T) have been successfully applied
in end-to-end speech recognition. However, the recurrent structure makes it
difficult for parallelization . In this paper, we propose a self-attention
transducer (SA-T) for speech recognition. RNNs are replaced with self-attention
blocks, which are powerful to model long-term dependencies inside sequences and
able to be efficiently parallelized. Furthermore, a path-aware regularization
is proposed to assist SA-T to learn alignments and improve the performance.
Additionally, a chunk-flow mechanism is utilized to achieve online decoding.
All experiments are conducted on a Mandarin Chinese dataset AISHELL-1. The
results demonstrate that our proposed approach achieves a 21.3% relative
reduction in character error rate compared with the baseline RNN-T. In
addition, the SA-T with chunk-flow mechanism can perform online decoding with
only a little degradation of the performance
Pattern formation of a pathway-based diffusion model: linear stability analysis and an asymptotic preserving method
We investigate the linear stability analysis of a pathway-based diffusion
model (PBDM), which characterizes the dynamics of the engineered Escherichia
coli populations [X. Xue and C. Xue and M. Tang, P LoS Computational Biology,
14 (2018), pp. e1006178]. This stability analysis considers small perturbations
of the density and chemical concentration around two non-trivial steady states,
and the linearized equations are transformed into a generalized eigenvalue
problem. By formal analysis, when the internal variable responds to the outside
signal fast enough, the PBDM converges to an anisotropic diffusion model, for
which the probability density distribution in the internal variable becomes a
delta function. We introduce an asymptotic preserving (AP) scheme for the PBDM
that converges to a stable limit scheme consistent with the anisotropic
diffusion model. Further numerical simulations demonstrate the theoretical
results of linear stability analysis, i.e., the pattern formation, and the
convergence of the AP scheme
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