Search Results for author:
Found 45 papers, 19 papers with code
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method.
Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning
We evaluate the performance of skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training data and three different learning strategies.
Exploring connections of spectral analysis and transfer learning in medical imaging
In this paper, we use spectral analysis to investigate transfer learning and study model sensitivity to frequency shortcuts in medical imaging.
Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
Transfer learning has become an essential part of medical imaging classification algorithms, often leveraging ImageNet weights.
Copycats: the many lives of a publicly available medical imaging dataset
Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare.
[Citation needed] Data usage and citation practices in medical imaging conferences
We applied both tools on a study of the usage of 20 publicly available medical datasets in papers from MICCAI and MIDL.
Augmenting Chest X-ray Datasets with Non-Expert Annotations
We train a chest drain detector with the non-expert annotations that generalizes well to expert labels.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Revisiting Hidden Representations in Transfer Learning for Medical Imaging
Our findings show that the similarity between networks before and after fine-tuning does not correlate with performance gains, suggesting that the advantages of transfer learning might not solely originate from the reuse of features in the early layers of a convolutional neural network.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays
The availability of large public datasets and the increased amount of computing power have shifted the interest of the medical community to high-performance algorithms.
Detection of Furigana Text in Images
This project focuses on detecting furigana in Japanese books and comics.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry
In the pharmaceutical industry, the maintenance of production machines must be audited by the regulator.
Effect of Prior-based Losses on Segmentation Performance: A Benchmark
To enforce anatomical plausibility, recent research studies have focused on incorporating prior knowledge such as object shape or boundary, as constraints in the loss function.
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources.
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification
There is currently no consensus on how to choose appropriate source data, and in the literature we can find both evidence of favoring large natural image datasets such as ImageNet, and evidence of favoring more specialized medical datasets.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
How I failed machine learning in medical imaging -- shortcomings and recommendations
Finally we provide a broad range of recommendations on how to further these address problems in the future.
Using uncertainty estimation to reduce false positives in liver lesion detection
We find that the use of a dropout rate of 0. 5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.
Crowdsourcing Airway Annotations in Chest Computed Tomography Images
We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall.
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks.
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
Therefore, we propose a detection and classification system for lung nodules in CT scans.
Risk of Training Diagnostic Algorithms on Data with Demographic Bias
Surprisingly, we found that papers focusing on diagnosis rarely describe the demographics of the datasets used, and the diagnosis is purely based on images.
Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning
Deep learning has led to state-of-the-art results for many medical imaging tasks, such as segmentation of different anatomical structures.
Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis
Machine learning has a recognised need for large amounts of annotated data.
A Survey of Crowdsourcing in Medical Image Analysis
Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis.
Cats or CAT scans: transfer learning from natural or medical image source datasets?
The source datasets do not have to be related to the target task.
Characterizing multiple instance datasets
When performing a comparison of different MIL classifiers, it is important to understand the differences of the datasets, used in the comparison.
Crowd disagreement about medical images is informative
Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds.
Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans
We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging.
Exploring the similarity of medical imaging classification problems
Supervised learning is ubiquitous in medical image analysis.
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually.
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
The classifier has a stronger correlation with PFT than the density based method, the percentage of emphysema in the intersection of annotations from both radiologists, and the percentage of emphysema annotated by one of the radiologists.
Label Stability in Multiple Instance Learning
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers.
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction.
Classification of COPD with Multiple Instance Learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate.
Transfer learning for multi-center classification of chronic obstructive pulmonary disease
We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks.
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag.
On Classification with Bags, Groups and Sets
To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors.
Quantile Representation for Indirect Immunofluorescence Image Classification
Considering one whole slide as a collection (a bag) of feature vectors, however, poses the problem of how to handle this bag.
Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors.
Multiple Instance Learning with Bag Dissimilarities
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous.
