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Machine Learning and Knowledge Discovery

INESC-ID · Rua Alves Redol 9, 1000-029 Lisboa, Portugal

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30 results

Using Soft Labels to Model Uncertainty in Medical Image Segmentation

Publication match 60%

CountPath: Automating Fragment Counting in Digital Pathology

Publication match 56%

CountPath: Automating Fragment Counting in Digital Pathology

Publication match 56%

Finding Regions of Interest in Whole Slide Images Using Multiple Instance Learning

Publication match 53%

Using Soft Labels to Model Uncertainty in Medical Image Segmentation

Publication match 52%

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

Publication match 48%

Multiple Instance Learning for WSI: A comparative analysis of attention-based approaches

Publication match 46%

Segmentation of X‐ray coronary angiography with an artificial intelligence deep learning model: Impact in operator visual assessment of coronary stenosis severity

Publication match 45%

Using a Siamese Network to Accurately Detect Ischemic Stroke in Computed Tomography Scans

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Potential and limitations of computed tomography images as predictors of the outcome of ischemic stroke events: a review

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Using Information Interaction to Discover Epistatic Effects in Complex Diseases

Publication match 38%

Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks

Publication match 37%

A Data Mining Approach for the Detection of High-Risk Breast Cancer Groups

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An analysis of the positional distribution of DNA motifs in promoter regions and its biological relevance

Publication match 36%

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

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Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

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Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation

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MUSA: a parameter free algorithm for the identification of biologically significant motifs

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Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance

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Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-ends

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Stenosis Detection in X-ray Coronary Angiography with Deep Neural Networks Leveraged by Attention Mechanisms

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Techniques for the creation of digital watermarks in sequential circuit designs

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Constraint Relaxations for Discovering Unknown Sequential Patterns

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Enhancing the Interpretation of Spirometry: Joint Utilization of <i>n</i>-Order Adaptive Fourier Decomposition and Deep Learning Techniques

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GRISOTTO: A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge

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Augmentation-Based Approaches for Overcoming Low Visibility in Street Object Detection

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Robust techniques for watermarking sequential circuit designs

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Artificial Intelligence Applications in Stroke

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Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness

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Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease

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