FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling

• URL: http://arxiv.org/abs/2310.02492v3
• Date: Fri, 12 Apr 2024 07:06:52 GMT
• Title: FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
• Authors: Yan Luo, Muhammad Osama Khan, Yu Tian, Min Shi, Zehao Dou, Tobias Elze, Yi Fang, Mengyu Wang,
• Abstract summary: We conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes. Our investigation spans both 2D and 3D models and evaluates fairness across five architectures on three common eye diseases. We release Harvard-FairVision, the first large-scale medical fairness dataset with 30,000 subjects featuring both 2D and 3D imaging data.
• Score: 19.16603153814857
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Equity in AI for healthcare is crucial due to its direct impact on human well-being. Despite advancements in 2D medical imaging fairness, the fairness of 3D models remains underexplored, hindered by the small sizes of 3D fairness datasets. Since 3D imaging surpasses 2D imaging in SOTA clinical care, it is critical to understand the fairness of these 3D models. To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes. Our investigation spans both 2D and 3D models and evaluates fairness across five architectures on three common eye diseases, revealing significant biases across race, gender, and ethnicity. To alleviate these biases, we propose a novel fair identity scaling (FIS) method that improves both overall performance and fairness, outperforming various SOTA fairness methods. Moreover, we release Harvard-FairVision, the first large-scale medical fairness dataset with 30,000 subjects featuring both 2D and 3D imaging data and six demographic identity attributes. Harvard-FairVision provides labels for three major eye disorders affecting about 380 million people worldwide, serving as a valuable resource for both 2D and 3D fairness learning. Our code and dataset are publicly accessible at \url{https://ophai.hms.harvard.edu/datasets/harvard-fairvision30k}.

Related papers

• E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model [23.56751925900571]
  The development of 3D medical vision-language models holds significant potential for disease diagnosis and patient treatment. We utilize self-supervised learning to construct a 3D visual foundation model for extracting 3D visual features. We apply 3D spatial convolutions to aggregate and project high-level image features, reducing computational complexity. Our model demonstrates superior performance compared to existing methods in report generation, visual question answering, and disease diagnosis.
  arXiv  Detail & Related papers  (2024-10-18T06:31:40Z)
• Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
  We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
  arXiv  Detail & Related papers  (2023-11-03T15:41:15Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• Cross-dimensional transfer learning in medical image segmentation with deep learning [0.4588028371034407]
  We introduce an efficient way to transfer the efficiency of a 2D classification network trained on natural images to 2D, 3D uni- and multi-modal medical image segmentation applications. In this paper, we designed novel architectures based on two key principles: weight transfer by embedding a 2D pre-trained encoder into a higher dimensional U-Net, and dimensional transfer by expanding a 2D segmentation network into a higher dimension one.
  arXiv  Detail & Related papers  (2023-07-29T02:50:38Z)
• Interpretable 2D Vision Models for 3D Medical Images [47.75089895500738]
  This study proposes a simple approach of adapting 2D networks with an intermediate feature representation for processing 3D images. We show on all 3D MedMNIST datasets as benchmark and two real-world datasets consisting of several hundred high-resolution CT or MRI scans that our approach performs on par with existing methods.
  arXiv  Detail & Related papers  (2023-07-13T08:27:09Z)
• Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization [13.792327874980632]
  We introduce Harvard Glaucoma Fairness (Harvard-GF), a dataset with both 2D and 3D data imaging and balanced racial groups for glaucoma detection. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and fairness tasks. We propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness.
  arXiv  Detail & Related papers  (2023-06-15T16:39:05Z)
• Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition [25.80846093248797]
  We propose to process the 3D data by a 2+1D framework so that we can exploit the powerful deep 2D Convolutional Neural Network (CNN) networks pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition. Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics are decomposed to 2D slices according to neighboring voxel positions. Global pooling is applied to remove redundant information as the activation patterns are sparsely distributed over feature maps. Channel-wise and slice-wise convolutions are proposed to aggregate the contextual information in the third dimension unprocessed by the 2D CNN model.
  arXiv  Detail & Related papers  (2022-11-21T15:22:59Z)
• Gait Recognition in the Wild with Dense 3D Representations and A Benchmark [86.68648536257588]
  Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. This paper aims to explore dense 3D representations for gait recognition in the wild. We build the first large-scale 3D representation-based gait recognition dataset, named Gait3D.
  arXiv  Detail & Related papers  (2022-04-06T03:54:06Z)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)
• 2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data [54.223614679807994]
  3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks. Applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D models. In this work, we proposed a novel 2D strategical representation of volumetric data, namely 2.75D. As a result, 2D CNN networks can also be used to learn volumetric information.
  arXiv  Detail & Related papers  (2020-02-11T08:24:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.