253 research outputs found
Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks
Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. Automation of the diagnosis process will enable accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Machine learning technologies have been used for automated diagnosis of malaria. We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks. First, we describe image processing methods used for segmentation of red blood cells from wholeslide images. We then discuss the procedures of compiling a pathologists-curated image dataset for training deep neural network, as well as data augmentation methods used to significantly increase the size of the dataset, in light of the overfitting problem associated with training deep convolutional neural networks. We will then compare the classification accuracies obtained by deep convolutional neural networks through training, validating, and testing with various combinations of the datasets. These datasets include the original dataset and the significantly augmented datasets, which are obtained using direct interpolation, as well as indirect interpolation using automatically extracted features provided by stacked autoencoders. This chapter ends with a discussion of further research
Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
Current class-incremental learning research mainly focuses on single-label
classification tasks while multi-label class-incremental learning (MLCIL) with
more practical application scenarios is rarely studied. Although there have
been many anti-forgetting methods to solve the problem of catastrophic
forgetting in class-incremental learning, these methods have difficulty in
solving the MLCIL problem due to label absence and information dilution. In
this paper, we propose a knowledge restore and transfer (KRT) framework for
MLCIL, which includes a dynamic pseudo-label (DPL) module to restore the old
class knowledge and an incremental cross-attention(ICA) module to save
session-specific knowledge and transfer old class knowledge to the new model
sufficiently. Besides, we propose a token loss to jointly optimize the
incremental cross-attention module. Experimental results on MS-COCO and PASCAL
VOC datasets demonstrate the effectiveness of our method for improving
recognition performance and mitigating forgetting on multi-label
class-incremental learning tasks
Review of 2D Animation Restoration in Visual Domain Based on Deep Learning
Traditional 2D animation is a distinct visual style with a production process and image characteristics that differ significantly from real-life scenes. It usually requires drawing pictures frame by frame and saving them as bitmaps. During the storage, transmission, and playback process, 2D animation may encounter problems such as picture quality degradation, insufficient resolution, and discontinuous timing. With the development of deep learning technology, it has been widely used in the field of animation restoration. This paper provides a comprehensive summary of 2D animation restoration based on deep learning. Firstly, exploring existing animation datasets can help identify the available data support and the bottleneck in establishing animation datasets. Secondly, investigating and testing deep learning-based algorithms for animation image quality restoration and animation interpolation can help identify key points and challenges in animation restoration. Additionally, introducing methods designed to ensure consistency between animation frames can provide insights for future animation video restoration. Analyzing the effectiveness of existing image quality assessment (IQA) methods for animation images can help identify practical IQA methods to guide restoration results. Finally, based on the above analysis, this paper clarifies the challenges in animation restoration tasks and presents future development directions of deep learning in animation restoration field
RTN: Reparameterized Ternary Network
To deploy deep neural networks on resource-limited devices, quantization has
been widely explored. In this work, we study the extremely low-bit networks
which have tremendous speed-up, memory saving with quantized activation and
weights. We first bring up three omitted issues in extremely low-bit networks:
the squashing range of quantized values; the gradient vanishing during
backpropagation and the unexploited hardware acceleration of ternary networks.
By reparameterizing quantized activation and weights vector with full precision
scale and offset for fixed ternary vector, we decouple the range and magnitude
from the direction to extenuate the three issues. Learnable scale and offset
can automatically adjust the range of quantized values and sparsity without
gradient vanishing. A novel encoding and computation pat-tern are designed to
support efficient computing for our reparameterized ternary network (RTN).
Experiments on ResNet-18 for ImageNet demonstrate that the proposed RTN finds a
much better efficiency between bitwidth and accuracy, and achieves up to 26.76%
relative accuracy improvement compared with state-of-the-art methods. Moreover,
we validate the proposed computation pattern on Field Programmable Gate Arrays
(FPGA), and it brings 46.46x and 89.17x savings on power and area respectively
compared with the full precision convolution.Comment: To appear at AAAI-2
Insulin resistance predicts progression of de novo atherosclerotic plaques in patients with coronary heart disease: a one-year follow-up study
BACKGROUND: The aim of our study was to explore and evaluate the relationship between insulin resistance and progression of coronary atherosclerotic plaques. With the great burden coronary heart disease is imposing on individuals, healthcare professionals have already embarked on determining its potential modifiable risk factors in the light of preventive medicine. Insulin resistance has been generally recognized as a novel risk factor based on epidemiological studies; however, few researches have focused on its effect on coronary atherosclerotic plaque progression. METHODS: From June 7, 2007 to December 30, 2011, 366 patients received their index coronary angiogram and were subsequently found to have coronary atherosclerotic plaques or normal angiograms were consecutively enrolled in the study by the department of cardiology at the Ruijin Hospital, which is affiliated to the Shanghai Jiaotong University School of Medicine. All patients had follow-up angiograms after the 1-year period for evaluating the progression of the coronary lesions. The modified Gensini score was adopted for assessing coronary lesions while the HOMA-IR method was utilized for determining the state of their insulin resistance. Baseline characteristics and laboratory test results were described and the binomial regression analysis was conducted to investigate the relationship between insulin resistance and coronary atherosclerotic plaque progression. RESULTS: Index and follow-up Gensini scores were similar between the higher insulin lower insulin resistant groups (9.09 ± 14.33 vs 9.44 ± 12.88, p = 0.813 and 17.21 ± 18.46 vs 14.09 ± 14.18, p =0.358). However the Gensini score assessing coronary lesion progression between both visits was significantly elevated in the higher insulin resistant group (8.13 ± 11.83 versus 4.65 ± 7.58, p = 0.019). Multivariate logistic binomial regression analysis revealed that insulin resistance (HOMA-IR > 3.4583) was an independent predictor for coronary arterial plaque progression (OR = 4.969, p = 0.011). We also divided all the participants into a diabetic (n = 136) and a non-diabetic group (n = 230), and HOMA-IR remained an independent predictor for atherosclerosis plaque progression. CONCLUSIONS: Insulin resistance is an independent predictor of atherosclerosis plaque progression in patients with coronary heart disease in both the diabetic and non-diabetic population
Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier
Automatic labeling of coronary arteries is an essential task in the practical
diagnosis process of cardiovascular diseases. For experienced radiologists, the
anatomically predetermined connections are important for labeling the artery
segments accurately, while this prior knowledge is barely explored in previous
studies. In this paper, we present a new framework called TopoLab which
incorporates the anatomical connections into the network design explicitly.
Specifically, the strategies of intra-segment feature aggregation and
inter-segment feature interaction are introduced for hierarchical segment
feature extraction. Moreover, we propose the anatomy-aware connection
classifier to enable classification for each connected segment pair, which
effectively exploits the prior topology among the arteries with different
categories. To validate the effectiveness of our method, we contribute
high-quality annotations of artery labeling to the public orCaScore dataset.
The experimental results on both the orCaScore dataset and an in-house dataset
show that our TopoLab has achieved state-of-the-art performance.Comment: Accepted by MICCAI 202
Identifying Latent Causal Content for Multi-Source Domain Adaptation
Multi-source domain adaptation (MSDA) learns to predict the labels in target
domain data, under the setting that data from multiple source domains are
labelled and data from the target domain are unlabelled. Most methods for this
task focus on learning invariant representations across domains. However, their
success relies heavily on the assumption that the label distribution remains
consistent across domains, which may not hold in general real-world problems.
In this paper, we propose a new and more flexible assumption, termed
\textit{latent covariate shift}, where a latent content variable
and a latent style variable are introduced in the generative
process, with the marginal distribution of changing across
domains and the conditional distribution of the label given
remaining invariant across domains. We show that although (completely)
identifying the proposed latent causal model is challenging, the latent content
variable can be identified up to scaling by using its dependence with labels
from source domains, together with the identifiability conditions of nonlinear
ICA. This motivates us to propose a novel method for MSDA, which learns the
invariant label distribution conditional on the latent content variable,
instead of learning invariant representations. Empirical evaluation on
simulation and real data demonstrates the effectiveness of the proposed method
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