Search Results for author:
Found 12 papers, 7 papers with code
Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
However, the domain shift from natural to medical images has prompted alternatives such as RadImageNet, often demonstrating comparable classification performance.
Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
We present "actionability" as a conceptual metric to reveal the data quality gap between characteristics of data on CCPs and the desired characteristics of data for AI 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.
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.
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.
Memory-aware curriculum federated learning for breast cancer classification
Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model.
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN).
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge.
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
Capsule Networks against Medical Imaging Data Challenges
A key component to the success of deep learning is the availability of massive amounts of training data.
