Develops deep learning for logical code segmentation.
problem Lack of logically segmented source code.
method Novel deep learning approach to generate logical code segments.
result Improves software analysis tasks like commenting, bug detection, and code synthesis.
Deep learning improves medical ultrasound image segmentation accuracy.
problem Improving accuracy in medical ultrasound image segmentation.
method Categorizes deep learning methods into six groups and analyzes current representative algorithms.
result Current methods show significant improvement in image segmentation accuracy.
Machine vision-guided 3D medical image compression improves segmentation accuracy.
problem High data traffic and computation costs in cloud-based medical image analysis.
method Developed a machine vision-oriented 3D image compression framework for medical segmentation.
result Significantly higher segmentation accuracy at the same compression rate or better compression rate under the same accuracy.
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.
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.
Method improves model performance on segments with local distribution shifts.
problem Improving model generalization across multiple data segments with local distribution differences.
method Two-stage multiply robust estimation method for tabular data analysis.
result Significantly improves prediction accuracy and robustness on regression and classification tasks.
FAST optimizes additive segmentation for faster, more interpretable models.
problem Efficiently segmenting and interpreting complex datasets.
method Optimization framework for fast piecewise constant shape functions.
result 2 orders of magnitude faster than state-of-the-art methods.
The paper reviews techniques for detecting errors in semantic segmentation models.
problem Detecting false positives and false negatives in semantic segmentation models.
method Uncertainty quantification techniques applied to semantic segmentation.
result Techniques for detecting false positives and false negatives are proposed and discussed.
Develops spatial uncertainty guarantees for image segmentation models.
problem Ensuring reliable segmentation predictions for biomedical images.
method Adapting conformal inference to imaging, using transformed logit scores and calibration datasets.
result Confidence sets provide spatial uncertainty guarantees with desired probability.
Method learns reusable actions from human demonstrations.
problem Learning reusable actions from human demonstrations.
method Skill segmentation, inverse reinforcement learning, one-class support vector machine.
result Inferred options improve learning and planning.
LcGAN generates synthetic CT images for hemorrhagic lesion segmentation.
problem Scarce training data for hemorrhagic lesion segmentation.
method Lesion conditional Generative Adversarial Network (LcGAN) for synthetic image generation.
result Segmentation improved by 12.8% with synthetic data augmentation.
Paper benchmarks machine learning for detecting process curve drifts.
problem Detecting drifts in multivariate manufacturing process data.
method Synthetic data generation and evaluation score introduction.
result Existing algorithms often fail with complex drift scenarios.
GCNs adapted for road networks improve performance on edge prediction tasks.
problem Improving machine learning on road networks for edge prediction tasks.
method Introducing Relational Fusion Network (RFN) for road networks.
result RFN outperforms state-of-the-art GCNs on road segment regression and classification tasks.
Experiment tested active learning for Thai text segmentation.
problem Using active learning for text annotation in machine learning.
method Live experiment with two sampling methods: random and model-based.
result Active learning strategy interacted with real-world changes.
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…
GANs improve echo frame generation with labeled patches.
problem Automated echocardiography with limited labels.
method Conditional GAN with patch-based discriminator.
result GAN-enhanced echo frames match given segmentation masks.
A new heuristic LM algorithm improves k-segmentation accuracy with less computation.
problem Efficiently segmenting large video streams into meaningful piecewise-linear segments.
method Inspired by Lloyd's and Lloyd-Max algorithms, LM algorithm iteratively minimizes a cost function.
result LM algorithm achieves competitive accuracy with exact methods at a fraction of the computational cost.
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
Deep learning model uses mixed supervision for brain tumor segmentation.
problem Costly manual tumor segmentation data.
method Extends segmentation networks with an image-level classification branch.
result Significant improvement in segmentation performance.
Algorithm segments glandular structures in colon histology images for cancer grading.
problem Manual gland segmentation is time-consuming and risky for patients.
method Local intensity and texture features, Random Forest classifier, multilevel approach.
result Fast, accurate automatic gland segmentation for clinical use.
Proposes a strategy to train models with minimal labeled data.
problem Scarce and expensive labeled data for medical tasks.
method Recursive training strategy to use image-level annotations for pixel-level segmentation.
result Improved segmentation of intracranial hemorrhage in CT scans.
Adversarial examples mislead deep learning models in medical image segmentation.
problem Vulnerability of deep learning models to adversarial examples in medical image segmentation.
method Proposed Adaptive Segmentation Mask Attack (ASMA) to craft targeted adversarial examples.
result Demonstrated vulnerability of deep learning models to adversarial examples in medical image segmentation.
Paper develops a method to identify minimal sample subspaces from limited data.
problem Challenging task of subspace segmentation with minimal sample subspaces.
method Develops a theoretical framework and optimization algorithms for MSS.
result The MSS model can retrieve minimal sample subspaces even when they are heavily intersected.
AxCell automates results extraction from machine learning papers.
problem Difficulty in tracking progress in machine learning due to many papers.
method AxCell uses table segmentation and learns structural knowledge to extract results.
result AxCell significantly improves results extraction compared to existing methods.
Unified approach for learning with weak labels across various tasks.
problem Learning with noisy or incomplete labels in diverse machine learning settings.
method Implicit posterior models for joint label inference.
result Unified training objective for various machine learning tasks.
Reinforcement learning improves self training for medical image segmentation.
problem Lack of labeled data in medical imaging.
method Integrating reinforcement learning into self training for complex segmentation networks.
result Improved segmentation performance with less labeled data.
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.
The segmentation of large scale power grids into zones is crucial for control room operators when managing the grid complexity near real time. In this paper we propose a new method in two steps which is able to automatically do this segmentation, while taking into account the real time context, in order to help them ha…
Most modern neural machine translation (NMT) systems rely on presegmented inputs. Segmentation granularity importantly determines the input and output sequence lengths, hence the modeling depth, and source and target vocabularies, which in turn determine model size, computational costs of softmax normalization, and han…
This paper adapts PATE for semantic segmentation while maintaining privacy.
problem Preserving privacy in medical machine learning, especially for sensitive information.
method Adapting PATE for semantic segmentation using low-dimensional representations and low-sensitivity queries.
result An Autoencoder-based PATE variant achieves a higher Dice coefficient for the same privacy guarantee.
Develops a method to segment high-dimensional data into regions with different local intrinsic dimensions.
problem Data often has varying intrinsic dimensions within the same dataset, challenging traditional analysis.
method Discovers regions with different local intrinsic dimensions and segments the data accordingly.
result Many real-world data sets contain regions with widely heterogeneous dimensions, which can be segmented using local intrinsic dimension.
This study assesses ML methods for brain tumor segmentation and survival prediction.
problem Segmenting and predicting outcomes of brain tumors with varying sub-regions and heterogeneous properties.
method Evaluation of state-of-the-art machine learning algorithms on BraTS challenge datasets.
result Identification of best ML algorithms for brain tumor segmentation and survival prediction.
Automated process links oral health to systemic conditions using machine learning.
problem Correlating oral health with systemic health conditions.
method Intraoral fluorescent biomarker imaging, machine learning segmentation, and clinical examination.
result Machine learning classifier achieved AUC of 0.677, indicating a learned association between disease signatures in images and periodontal disease.
Framework for ensuring fairness in machine learning models across multiple groups.
problem Ensuring fairness in machine learning models across multiple groups.
method Introduces (s,G,α)−GMC for multi-dimensional mappings and constraint sets, proposing algorithms to achieve multicalibration. result Demonstrates the effectiveness of the framework on various scenarios, including image segmentation, hierarchical classification, and text generation.
The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole. In this work, we present an analysis work-flow for fully-…
A tool simplifies neural network training for medical image analysis.
problem Difficulties in training neural networks for medical image analysis.
method Intuitive interface for WSI annotation and display, human-in-the-loop strategy.
result Improved network performance through iterative annotation.
This review chronicles AI algorithms for cervical cancer screening.
problem Automated screening of cervical cancer using AI methods.
method Analysis of various machine learning algorithms and clustering techniques.
result Holistic review of computational methods over time.
Paper proposes a weak supervision technique for CNN semantic segmentation of lung diseases using partially annotated data.
problem Creating annotated datasets for semantic segmentation of lung diseases is laborious and time-consuming.
method Proposes a weak supervision technique that utilizes partially annotated datasets to improve CNN semantic segmentation accuracy.
result Significantly improved segmentation accuracy using partially annotated datasets.
Enhances FM models for numerical features using function basis encoding.
problem Challenges in incorporating numerical features into FM variants.
method Encoding numerical features into a vector of function values for learning segmentized functions.
result Improves model accuracy by learning segmentized functions of numerical features.
The paper uses RFM and clustering to segment bank customers.
problem Challenges in customer retention and profitable segmentation in banking.
method RFM technique and clustering algorithms applied to real data.
result Successful customer segmentation improves conversion rates.
Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. However, there does not often exist a good model on the whole dataset, so we seek to find a small subset where there exists a useful model. We ar…
Migration crisis, climate change or tax havens: Global challenges need global solutions. But agreeing on a joint approach is difficult without a common ground for discussion. Public spheres are highly segmented because news are mainly produced and received on a national level. Gain- ing a global view on international d…
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.
SODA optimizes data augmentation allocation for deep learning models.
problem Inefficient allocation of data augmentation budget in deep neural networks.
method Online learning to dynamically allocate data augmentation budget during training.
result Optimized data augmentation can save computation time and promote greener machine learning.
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…
A machine learning method predicts rock permeability from 3D images.
problem Efficiently predict permeability of heterogeneous rocks for planetary and robotic applications.
method Machine learning guided 3D properties recognition of rock morphology from 3D micro CT and MRI images.
result The morphology decoder method accurately predicts permeability from 3D images.
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.
This paper distills a complex travel mode choice model into simpler, interpretable models.
problem Lack of interpretability in complex machine learning models for travel behavior.
method Model distillation combined with market segmentation.
result Generated interpretable models that closely match the predictions of the original complex model.