DNA rearrangement processes recombine gene segments that are organized on the chromosome in a variety of ways. The segments can overlap, interleave or one may be a subsegment of another. We use directed graphs to represent segment organizations on a given locus where contigs containing rearranged segments represent ver…
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48 results for “Segmentation Analysis”
EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networkscs.LG
EM-GAN uses GANs for fast stress analysis of multi-segment interconnects.
problem Fast and accurate stress analysis for EM failure assessment in multi-segment interconnects.
method Conditional GAN model trained on images of multi-segment wires and current densities.
result EM-GAN provides accurate stress distribution with 6.6% error and 8.3X speedup.
New method uses contours of segmented images for X-ray classification.
problem Classifying X-ray images of segmented radiography.
method Develops a new approach for image analysis of multivariate planar curves, addressing alignment issues.
result Demonstrates the robustness and appeal of the proposed method through detection of cardiomegaly and numerical experiments.
Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approachcs.CL
Study uses LLM to extract and compare segment disclosures from financial filings.
problem Challenges in completeness and comparability of segment disclosures in financial reports.
method Developed a large language model framework to extract and preserve segment information from Form 10-K filings.
result The LLM accurately extracts segment-level information and addresses cross-period knowledge questions.
This study benchmarks algorithms for automatic segmentation of LGE-MRI images of the left atrium.
problem Challenging segmentation of LGE-MRI images due to low contrast.
method Organized a large-scale benchmarking challenge with 154 3D LGE-MRIs and 27 teams.
result Top method achieved 93.2% dice score and 0.7 mm mean surface to surface distance.
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
problem Difficulty in computing utility functions for high-player energy game-theoretic frameworks.
method Graphical Lasso based approach to cluster features leading to energy usage behaviors.
result Characteristic clusters demonstrating different energy usage behaviors identified.
BiPE blends intra-segment and inter-segment encodings for better length extrapolation.
problem Improving length extrapolation in language models.
method Bilevel Positional Encoding (BiPE) that separates intra-segment and inter-segment encodings.
result BiPE enhances length extrapolation across various text modalities.
Many software analysis methods have come to rely on machine learning approaches. Code segmentation - the process of decomposing source code into meaningful blocks - can augment these methods by featurizing code, reducing noise, and limiting the problem space. Traditionally, code segmentation has been done using syntact…
3D U-Net improves kidney and tumor segmentation from CT scans.
problem Manual segmentation by clinicians is laborious and error-prone.
method Multi-scale supervised 3D U-Net with deep supervision and post-processing.
result MSS U-Net achieves high Dice coefficients (0.969 for kidney, 0.805 for tumor) on KiTS19 dataset.
Framework automates microstructure image analysis for materials science.
problem Complex microstructures in materials require automated analysis.
method Combines unsupervised and supervised learning for classification and segmentation.
result Framework can automatically segment and classify micrographs.
This survey analyzes deep learning methods for real-time semantic image segmentation.
problem Efficient and accurate semantic image segmentation for real-time applications.
method Comprehensive analysis of deep learning architectures and techniques for fast inference.
result Best-performing architectures and techniques for real-time semantic image segmentation.
This study conducts a comprehensive analysis of time series segmentation on the Japanese stock prices listed on the first section of the Tokyo Stock Exchange during the period from 4 January 2000 to 30 January 2012. A recursive segmentation procedure is used under the assumption of a Gaussian mixture. The daily number …
We present a new approach to harmonic analysis that is trained to segment music into a sequence of chord spans tagged with chord labels. Formulated as a semi-Markov Conditional Random Field (semi-CRF), this joint segmentation and labeling approach enables the use of a rich set of segment-level features, such as segment…
Novel method segments heart sound signals using LSTMs with attention.
problem Heart sound segmentation for diagnostic applications.
method Bidirectional LSTMs with attention mechanisms.
result State-of-the-art performance on multiple benchmarks.
System segments Form 10-K documents into Item sections for financial analysis.
problem Segmenting Form 10-K documents into Item sections for efficient financial analysis.
method Developed an automatic Form 10-K Itemization system using NLP techniques.
result System achieves a retrieval rate of 93% for segmenting Item sections.
This paper explains the method used in the segmentation challenge (Task 1) in the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards Melanoma Detection challenge held in 2018. We have trained a U-Net network to perform the segmentation. The key elements for the training were first to adjust …
Generative Synthetic Augmentation using Label-to-Image Translation for Nuclei Image Segmentationcs.LG
This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.
problem Rare and time-consuming labeling of tumor nuclei images for semantic segmentation.
method Label-to-image translation to generate synthetic images.
result Synthetic augmentation improves segmentation accuracy.
The telecommunications industry is highly competitive, which means that the mobile providers need a business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal level of cost in marketing activities. Machine learning applications can be used to provide guidance on marketing…
Lung segmentation accuracy varies little across diverse datasets.
problem Limited clinical applicability of automated lung segmentation methods.
method Comparison of four deep learning approaches and two standard algorithms on diverse datasets.
result Standard U-net approach yields higher accuracy on routine imaging data.
We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, , is chosen by the user and optimally distributed am…
Framework for discovering treatment benefits in user segments.
problem Discovering differential impacts of treatments across user subgroups.
method Combines causal inference and machine learning for user segment discovery.
result Unified approach for treatment benefit discovery and assignment.
Coronary Artery Segmentation from Intravascular Optical Coherence Tomography Using Deep Capsulescs.LG
DeepCap automates coronary artery segmentation from IVOCT images.
problem Automated segmentation of coronary arteries from IVOCT images is challenging.
method Developed a deep learning method based on capsules for robust, unbiased segmentation.
result DeepCap achieves segmentation quality comparable to state-of-the-art methods.
We present an instance segmentation algorithm trained and applied to a CCTV recording of beef cattle during a winter finishing period. A fully convolutional network was transformed into an instance segmentation network that learns to label each instance of an animal separately. We introduce a conceptually simple framew…
CAggNet improves medical image segmentation by fusing coarse and fine features.
problem Medical image segmentation accuracy and efficiency.
method Crossing Aggregation Network with nested skip connections and weighted aggregation.
result CAggNet achieves more accurate and efficient segmentation compared to existing methods.
Proposes a new network to improve nuclei segmentation in histopathology images.
problem Challenges in separating overlapped nuclei in histopathology images.
method Introduces a bending loss regularized network to minimize contour points with large curvatures.
result Outperforms six state-of-the-art approaches on five quantitative metrics.
System uses TDA for user segmentation and demand forecasting.
problem User loyalty and demand forecasting challenges.
method TDA-based clustering of time series data with matrix factorization.
result Significantly higher accuracy in clustering and demand forecasting.
In this paper, we consider the problem of unsupervised video object segmentation via background subtraction. Specifically, we pose the nonsemantic extraction of a video's moving objects as a nonconvex optimization problem via a sum of sparse and low-rank matrices. The resulting formulation, a nonnegative variant of rob…
This paper compares stationarity in Bitcoin and S&P500 price indices.
problem Comparing stationarity in cryptocurrency and traditional stock market indices.
method Wide sense stationarity defined; Wiener-Khinchin Theorem applied; stationarity achieved through detrending and normalization of price returns.
result S&P500 price return achieves stationarity for 28 years with specific normalization windows, while Bitcoin's stationarity varies by segment and volatility.
This study considers the multivariate segmentation procedure under the assumption of the multivariate Gaussian mixture. Jensen-Shannon divergence between two multivariate Gaussian distributions is employed as a discriminator and a recursive segmentation procedure is proposed. The daily log-return time series for 30 cur…
Unsupervised fire and smoke segmentation from IR videos.
problem Early detection of fire from infrared videos.
method Spatial, temporal, and motion information fusion using optical flow, divergence, and intensity values.
result Markov Random Field outperforms other clustering algorithms in fire and smoke segmentation.
Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples. Adversarial examples are carefully crafted samples that force machine learning models to make mistakes during testing time. These maliciou…
DVNet efficiently segments large neurovascular datasets using skip connections.
problem Challenges in segmenting large neurovascular datasets from high-throughput microscopy data.
method A fully-convolutional, deep, and densely-connected encoder-decoder network with skip connections.
result DVNet achieves superior performance in semantic segmentation of neurovascular datasets.
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks have dominated the state of the art in this task. However, since this problem presents a challenging environment due to high variability in th…
Lung segmentation from abnormal CXRs using data imputation.
problem Segmenting lungs from CXRs with high opacity caused by respiratory ailments.
method Modified CNN-based segmentation network with deep generative model for data imputation.
result The model can segment lungs from abnormal CXRs, extending to cases with extreme abnormalities.
We introduce a new multi-dimensional nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for image segmentation. Based on the theory of sparse signal recovery, piecewise flat embedding with diverse channels attempts to recover a piecewise constant image representation with sparse region boundaries and sparse clust…
White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used…
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the …
MOCA enables meta-learning without task segmentation for online learning.
problem Meta-learning without task segmentation for online learning.
method MOCA combines meta-learning with online changepoint analysis.
result MOCA enables efficient meta-learning in time-varying tasks.
Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the…
U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentationeess.IV
U-Det improves lung nodule segmentation in CT images.
problem Challenging shapes and surroundings of lung nodules in CT images.
method End-to-end deep learning with Bi-FPN, Mish activation, and class weights.
result U-Det achieves 82.82% Dice similarity coefficient, comparable to human experts.
The Lady Maisry ballads afford us a framework within which to segment a storyline into its major components. Segments and as a consequence nodal points are discussed for nine different variants of the Lady Maisry story of a (young) woman being burnt to death by her family, on account of her becoming pregnant by a forei…
PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samplesstat.ML
Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample types. We propose PeakSegJoint, a new constrained maximum likelihood segmentation model for any number of sample types. To select the number of…
Study improves CNN medical image segmentation accuracy and reliability.
problem Over-confident predictions and silent failures in out-of-distribution data.
method Multi-task learning and spectral analysis of CNN feature maps.
result Joint multi-task learning models outperform dedicated models and detect OOD data more accurately.
QC methods improve reliability of machine learning-based image segmentation.
problem Inaccuracies in machine learning algorithms limit their clinical applicability.
method Analysis and validation of QC approaches for automatic segmentation.
result Aggregation of uncertainty and Dice prediction methods improved segmentation reliability.
Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from H…
Cloud based medical image analysis has become popular recently due to the high computation complexities of various deep neural network (DNN) based frameworks and the increasingly large volume of medical images that need to be processed. It has been demonstrated that for medical images the transmission from local to clo…
This review synthesizes uncertainty modeling in probabilistic image segmentation.
problem Relaxed Bayesian assumptions lead to missing uncertainty information in deep models.
method Standardizes theory, notation, and terminology for feature- and parameter-distribution modeling.
result Establishes a common framework for robust decision-making in segmentation tasks.
Traditionally, many text-mining tasks treat individual word-tokens as the finest meaningful semantic granularity. However, in many languages and specialized corpora, words are composed by concatenating semantically meaningful subword structures. Word-level analysis cannot leverage the semantic information present in su…