Source Themes

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

arXiv preprint Overview of the CXR-LT challenge on long-tailed, multi-label disease classification from chest X-ray.

Improving model fairness in image-based computer-aided diagnosis

Nature Communications A novel loss function for improved group fairness in chest X-ray classification.

Severe aortic stenosis detection by deep learning applied to echocardiography

European Heart Journal Accurate and generalizable detection of severe aortic stenosis based on single-view echocardiography.

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

MICCAI 2023 An investigation of pruning's impact on long-tailed, multi-label medical image classifiers.

Improved Multimodal Fusion for Small Datasets with Auxiliary Supervision

IEEE ISBI 2023 Parameter-efficient multimodal learning of histopathology imaging and clinical risk factors for improved prostate cancer classification.

Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays

IEEE Transactions on Medical Imaging A Transformer for weakly supervised disease localization that uses a novel feedback loop between (local) radiomics features and (global) chest X-ray features.

Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study

MICCAI Workshop on Data Augmentation, Labelling, and Imperfections 2022 [Oral] A large-scale benchmark for long-tailed learning of chest X-rays.

Self-Supervised Learning of Echocardiogram Videos Enables Data-Efficient Clinical Diagnosis

arXiv Preprint Self-supervised learning method for echocardiogram videos drives label-efficient fine-tuning on aortic stenosis classification. Presented at IMLH 2022, an ICML workshop.

Avalanche decision schemes to improve pediatric rib fracture detection

SPIE Medical Imaging 2022 Domain knowledge-guided method for improved rib fracture localization.

End-to-End Learning of Fused Image and Non-Image Features for Improved Breast Cancer Classification from MRI

CVAMD 2021 (ICCV Workshop) Methods for jointly learning from breast imaging and tabular non-image data to predict breast cancer.