74 research outputs found
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification
Decisions made by convolutional neural networks(CNN) can be understood and
explained by visualizing discriminative regions on images. To this end, Class
Activation Map (CAM) based methods were proposed as powerful interpretation
tools, making the prediction of deep learning models more explainable,
transparent, and trustworthy. However, all the CAM-based methods (e.g., CAM,
Grad-CAM, and Relevance-CAM) can only be used for interpreting CNN models with
fully-connected (FC) layers as a classifier. It is worth noting that many deep
learning models classify images without FC layers, e.g., few-shot learning
image classification, contrastive learning image classification, and image
retrieval tasks. In this work, a post-hoc interpretation tool named feature
activation map (FAM) is proposed, which can interpret deep learning models
without FC layers as a classifier. In the proposed FAM algorithm, the
channel-wise contribution weights are derived from the similarity scores
between two image embeddings. The activation maps are linearly combined with
the corresponding normalized contribution weights, forming the explanation map
for visualization. The quantitative and qualitative experiments conducted on
ten deep learning models for few-shot image classification, contrastive
learning image classification and image retrieval tasks demonstrate the
effectiveness of the proposed FAM algorithm.Comment: 14 page
Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection
Detecting weak target is an important and challenging problem in many
applications such as radar, sonar etc. However, conventional detection methods
are often ineffective in this case because of low signal-to-noise ratio (SNR).
This paper presents a track-before-detect (TBD) algorithm based on an improved
particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns
the problem of target detection to the problem of two-layer hypothesis testing.
The first layer is implemented by CRPFB for state estimation of possible
target. CRPFB has entirely parallel structure, consisting amounts of
cost-reference particle filters with different hypothesized prior information.
The second layer is to compare a test metric with a given threshold, which is
constructed from the output of the first layer and fits GEV distribution. The
performance of our proposed TBD algorithm and the existed TBD algorithms are
compared according to the experiments on nonlinear frequency modulated (NLFM)
signal detection and tracking. Simulation results show that the proposed TBD
algorithm has better performance than the state-of-the-arts in detection,
tracking, and time efficiency
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review
Interest point detection is one of the most fundamental and critical problems
in computer vision and image processing. In this paper, we carry out a
comprehensive review on image feature information (IFI) extraction techniques
for interest point detection. To systematically introduce how the existing
interest point detection methods extract IFI from an input image, we propose a
taxonomy of the IFI extraction techniques for interest point detection.
According to this taxonomy, we discuss different types of IFI extraction
techniques for interest point detection. Furthermore, we identify the main
unresolved issues related to the existing IFI extraction techniques for
interest point detection and any interest point detection methods that have not
been discussed before. The existing popular datasets and evaluation standards
are provided and the performances for eighteen state-of-the-art approaches are
evaluated and discussed. Moreover, future research directions on IFI extraction
techniques for interest point detection are elaborated
Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography
PURPOSE: Classic encoder-decoder-based convolutional neural network (EDCNN) approaches cannot accurately segment detailed anatomical structures of the mandible in computed tomography (CT), for instance, condyles and coronoids of the mandible, which are often affected by noise and metal artifacts. The main reason is that EDCNN approaches ignore the anatomical connectivity of the organs. In this paper, we propose a novel CNN-based 3D mandible segmentation approach that has the ability to accurately segment detailed anatomical structures. METHODS: Different from the classic EDCNNs that need to slice or crop the whole CT scan into 2D slices or 3D patches during the segmentation process, our proposed approach can perform mandible segmentation on complete 3D CT scans. The proposed method, namely, RCNNSeg, adopts the structure of the recurrent neural networks to form a directed acyclic graph in order to enable recurrent connections between adjacent nodes to retain their connectivity. Each node then functions as a classic EDCNN to segment a single slice in the CT scan. Our proposed approach can perform 3D mandible segmentation on sequential data of any varied lengths and does not require a large computation cost. The proposed RCNNSeg was evaluated on 109 head and neck CT scans from a local dataset and 40 scans from the PDDCA public dataset. The final accuracy of the proposed RCNNSeg was evaluated by calculating the Dice similarity coefficient (DSC), average symmetric surface distance (ASD), and 95% Hausdorff distance (95HD) between the reference standard and the automated segmentation. RESULTS: The proposed RCNNSeg outperforms the EDCNN-based approaches on both datasets and yields superior quantitative and qualitative performances when compared to the state-of-the-art approaches on the PDDCA dataset. The proposed RCNNSeg generated the most accurate segmentations with an average DSC of 97.48%, ASD of 0.2170 mm, and 95HD of 2.6562 mm on 109 CT scans, and an average DSC of 95.10%, ASD of 0.1367 mm, and 95HD of 1.3560 mm on the PDDCA dataset. CONCLUSIONS: The proposed RCNNSeg method generated more accurate automated segmentations than those of the other classic EDCNN segmentation techniques in terms of quantitative and qualitative evaluation. The proposed RCNNSeg has potential for automatic mandible segmentation by learning spatially structured information
Simultaneous conversion of all cell wall components by an oleaginous fungus without chemi-physical pretreatment
Lignin utilization during biomass conversion has been a major challenge for lignocellulosic biofuel. In particular, the conversion of lignin along with carbohydrate for fungible fuels and chemicals will both improve the overall carbon efficiency and reduce the need for chemical pretreatments. However, few biomass-converting microorganisms have the capacity to degrade all cell wall components including lignin, cellulose, and hemicellulose. We hereby evaluated a unique oleaginous fungus strain, Cunninghamella echinulata FR3, for its capacity to degrade lignin during biomass conversion to lipid, and the potential to carry out consolidated fermentation without chemical pretreatment, especially when combined with sorghum (Sorghum bicolor) bmr mutants with reduced lignin content. The study clearly showed that lignin was consumed together with carbohydrate during biomass conversion for all sorghum samples, which indicates that this organism has the potential for biomass conversion without chemical pretreatment. Even though dilute acid pretreatment of biomass resulted in more weight loss during fungal fermentation than untreated biomass, the lipid yields were comparable for untreated bmr6/bmr12 double mutant and dilute acid-pretreated wild-type biomass samples. The mechanisms for lignin degradation in oleaginous fungi were further elucidated through transcriptomics and chemical analysis. The studies showed that in C. echinulata FR3, the Fenton reaction may play an important role in lignin degradation. This discovery is among the first to show that a mechanism for lignin degradation similar to those found in white and brown rot basidiomycetous fungi exists in an oleaginous fungus. This study suggests that oleaginous fungi such as C. echinulata FR3 can be employed for complete biomass utilization in a consolidated platform without chemical pretreatment or can be used to convert lignin waste into lipids
CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma
Background: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches.Purpose: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT).Methods: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans.Results: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. Conclusion: MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.</p
MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study
<p>Abstract</p> <p>Background</p> <p>The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.</p> <p>Results</p> <p>Three comparative experiments are designed to evaluate the performance of MegaSNPHunter. The first experiment uses synthetic data generated on the basis of epistasis models. The second one uses a genome wide study on Parkinson disease (data acquired by using Illumina HumanHap300 SNP chips). The third one chooses the rheumatoid arthritis study from Wellcome Trust Case Control Consortium (WTCCC) using Affymetrix GeneChip 500K Mapping Array Set. MegaSNPHunter outperforms the best solution in this area and reports many potential interactions for the two real studies.</p> <p>Conclusion</p> <p>The experimental results on both synthetic data and two real data sets demonstrate that our proposed approach outperforms the best solution that is currently available in handling large-scale SNP data both in terms of speed and in terms of detection of potential interactions that were not identified before. To our knowledge, MegaSNPHunter is the first approach that is capable of identifying the disease-associated SNP interactions from WTCCC studies and is promising for practical disease prognosis.</p
An efficient approach of controlling traffic congestion in scale-free networks
We propose and study a model of traffic in communication networks. The
underlying network has a structure that is tunable between a scale-free growing
network with preferential attachments and a random growing network. To model
realistic situations where different nodes in a network may have different
capabilities, the message or packet creation and delivering rates at a node are
assumed to depend on the degree of the node. Noting that congestions are more
likely to take place at the nodes with high degrees in networks with scale-free
character, an efficient approach of selectively enhancing the
message-processing capability of a small fraction (e.g. 3%) of the nodes is
shown to perform just as good as enhancing the capability of all nodes. The
interplay between the creation rate and the delivering rate in determining
non-congested or congested traffic in a network is studied more numerically and
analytically.Comment: 7 pages, 5 figure
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