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Found 42 papers, 14 papers with code
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
A recent study showed that using the graphical structure underlying EHR data (e. g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction.
Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification
Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations.
Modelling EHR timeseries by restricting feature interaction
Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests.
Deep Physiological State Space Model for Clinical Forecasting
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements.
Neural Wireframe Renderer: Learning Wireframe to Image Translations
To this end, we propose a novel model based on a structure-appearance joint representation learned from both images and wireframes.
Selective Synthetic Augmentation with Quality Assurance
To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images.
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness.
Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.
Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss
When training the parameters of a linear dynamical model, the gradient descent algorithm is likely to fail to converge if the squared-error loss is used as the training loss function.
SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos
In this paper, we propose a semi-supervised body parsing model, termed SiamParseNet (SPN), to jointly learn single frame body parsing and label propagation between frames in a semi-supervised fashion.
Synthetic Sample Selection via Reinforcement Learning
However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images.
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
The paradigm of pretraining' from a set of relevant auxiliary tasks and thenfinetuning' on a target task has been successfully applied in many different domains.
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives
In many applications of computer graphics, art and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph or layout, and have a computer system automatically generate photo-realistic images that adhere to the input content.
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Histopathological analysis is the present gold standard for precancerous lesion diagnosis.
A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis
Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks.
Neural Stochastic Dual Dynamic Programming
Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks.
Coordinate Translator for Learning Deformable Medical Image Registration
The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.
Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach
With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.
Disentangling A Single MR Modality
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.
Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning
We present such a machine learning approach for analyzing Raman spectra of human and avian viruses.
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans
In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation.
End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement
In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence.
HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation
However, they have a major drawback that the generated JND is assessed in the real-world signal domain instead of in the perceptual domain in the human brain.
Deep filter bank regression for super-resolution of anisotropic MR brain images
In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement
Solving the problem of poor low-light image quality can effectively improve the visual quality of images and better improve the usability of computer vision.
Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis
The novel design of dual branches combines the strengths of deformation-grid-based transformation and warp-free generation for better identity preservation and robustness to occlusion in the synthesized videos.
Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment
We collected a new clinical IMV dataset with GMA annotations, and our experiments show that SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset and their results can significantly boost the CRNN-based GMA prediction performance.
Learning to Optimize with Stochastic Dominance Constraints
In real-world decision-making, uncertainty is important yet difficult to handle.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
Furthermore, HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Segmenting thalamic nuclei from manifold projections of multi-contrast MRI
After registration of these contrasts and isolation of the thalamus, we use the uniform manifold approximation and projection (UMAP) method for dimensionality reduction to produce a low-dimensional representation of the data within the thalamus.
A latent space for unsupervised MR image quality control via artifact assessment
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.
Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly
Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms.
The First Comprehensive Dataset with Multiple Distortion Types for Visual Just-Noticeable Differences
To benefit JND modeling, this work establishes a generalized JND dataset with a coarse-to-fine JND selection, which contains 106 source images and 1, 642 JND maps, covering 25 distortion types.
Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations
Data-driven thalamic nuclei parcellation depends on high-quality manual annotations.
Rapid Brain Meninges Surface Reconstruction with Layer Topology Guarantee
The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura.
Securing Deep Generative Models with Universal Adversarial Signature
Prior research attempted to mitigate these threats by detecting generated images, but the varying traces left by different generative models make it challenging to create a universal detector capable of generalizing to new, unseen generative models.
Synthetic Augmentation with Large-scale Unconditional Pre-training
To further reduce the dependency on annotated data, we propose a synthetic augmentation method called HistoDiffusion, which can be pre-trained on large-scale unlabeled datasets and later applied to a small-scale labeled dataset for augmented training.
Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach
Annotating biomedical images for supervised learning is a complex and labor-intensive task due to data diversity and its intricate nature.
Large Language Models can Learn Rules
In the deduction stage, the LLM is then prompted to employ the learned rule library to perform reasoning to answer test questions.
3D-Aware Talking-Head Video Motion Transfer
In this paper, we propose a novel 3D-aware talking-head video motion transfer network, Head3D, which fully exploits the subject appearance information by generating a visually-interpretable 3D canonical head from the 2D subject frames with a recurrent network.
