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Found 23 papers, 2 papers with code
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient.
Self-supervised Learning from 100 Million Medical Images
Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples.
You May Need both Good-GAN and Bad-GAN for Anomaly Detection
A Bad-GAN generates pseudo anomalies at the low-density area of inlier distribution, and thus the inlier/outlier distinction can be approximated.
Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities.
Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e. g., by 8% to 0. 91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs.
Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort
Training/testing cohorts included 927/100 COVID-19, 388/33 ILD, 189/33 other pneumonias, and 559/34 normal (no pathologies) CTs.
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
Additionally, we leverage location priors derived from manually labeled COVID-19 chest CTs patients to generate appropriate abnormality distributions.
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks
To make the network more robust to unanticipated noise perturbations, we use PGD to search for noise patterns that can trigger the network to give over-confident mistakes.
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space.
One-to-one Mapping for Unpaired Image-to-image Translation
Here we propose a self-inverse network learning approach for unpaired image-to-image translation.
Generative Adversarial Network
Image-to-Image Translation
+1
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
We argue that explicitly learning the classification uncertainty as an orthogonal measure to the predicted output, is essential to account for the inherent variability characteristic of this data.
Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities.
3D Organ Shape Reconstruction from Topogram Images
Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance.
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations.
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
The output imbalance refers to the imbalance between the false positives and false negatives of the inference model.
Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
To foster future research we demonstrate the limitations of the current benchmarking setup and provide new reference patient-wise splits for the used data sets.
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.
Shaping the Future through Innovations: From Medical Imaging to Precision Medicine
Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up.
A Self-Taught Artificial Agent for Multi-Physics Computational Model Personalization
Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model.
