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Found 21 papers, 8 papers with code
Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation
We conducted a prospective study to measure the clinical impact of an explainable machine learning system on interobserver agreement in chest radiograph interpretation.
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels.
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0. 6989 (95% CI 0. 6740, 0. 7240), AUC of 0. 7912, sensitivity of 0. 7064 and specificity of 0. 8760 for the abnormal diagnosis in general.
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
For the localization task with 14 types of lesions, our free-response receiver operating characteristic (FROC) analysis showed that the VinDr-CXR achieved a sensitivity of 80. 2% at the rate of 1. 0 false-positive lesion identified per scan.
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment.
Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling
This work aims at developing and validating a precise, fast multi-phase classifier to recognize three main types of contrast phases in abdominal CT scans.
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases.
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks.
PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children
To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases.
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
As a result, the labeled data may contain a variety of human biases with a high rate of disagreement among annotators, which significantly affect the performance of supervised machine learning algorithms.
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset).
DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans
This challenge raises the need for an automated and efficient approach to classifying body parts from X-ray scans.
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise.
VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays
We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans.
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
It demonstrates an area under the receiver operating characteristic curve (AUROC) of 88. 61% (95% CI 87. 19%, 90. 02%) for the image-level classification task and a mean average precision (mAP@0. 5) of 33. 56% for the lesion-level localization task.
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs.
Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels
The chest X-rays (CXRs) is one of the views most commonly ordered by radiologists (NHS), which is critical for diagnosis of many different thoracic diseases.
A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans
We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset.
Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels
The performance is on average better than 2. 6 out of 3 other individual radiologists with a mean AUC of 0. 930, which ranks first on the CheXpert leaderboard at the time of writing this paper.
Ranked #2 on
Multi-Label Classification
on CheXpert
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
When the projector is replaced with a CNN, we propose a relaxed PGD, which always converges.
Learning Convex Regularizers for Optimal Bayesian Denoising
We propose a data-driven algorithm for the maximum a posteriori (MAP) estimation of stochastic processes from noisy observations.
