Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

• URL: http://arxiv.org/abs/2012.03173v1
• Date: Sun, 6 Dec 2020 03:41:51 GMT
• Title: Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
• Authors: Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang
• Abstract summary: We propose a new margin-based surrogate loss function for the AUC score. It is more robust than the commonly used. square loss while enjoying the same advantage in terms of large-scale optimization. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets.
• Score: 63.44396343014749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep AUC Maximization (DAM) is a paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focus on the perspective of optimization by designing efficient stochastic algorithms, and studies on generalization performance of DAM on difficult tasks are missing. In this work, we aim to make DAM more practical for interesting real-world applications (e.g., medical image classification). First, we propose a new margin-based surrogate loss function for the AUC score (named as the AUC margin loss). It is more robust than the commonly used AUC square loss, while enjoying the same advantage in terms of large-scale stochastic optimization. Second, we conduct empirical studies of our DAM method on difficult medical image classification tasks, namely classification of chest x-ray images for identifying many threatening diseases and classification of images of skin lesions for identifying melanoma. Our DAM method has achieved great success on these difficult tasks, i.e., the 1st place on Stanford CheXpert competition (by the paper submission date) and Top 1% rank (rank 33 out of 3314 teams) on Kaggle 2020 Melanoma classification competition. We also conduct extensive ablation studies to demonstrate the advantages of the new AUC margin loss over the AUC square loss on benchmark datasets. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets.

Related papers

• Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective [14.39502951611029]
  We propose a fusion quality loss function that combines Structural Similarity Index Measure loss with l1 loss, offering a more comprehensive evaluation of reconstruction quality. We also introduce a data pre-processing strategy that enhances the average intensity ratio (AIR) between normal and abnormal regions, further improving the distinction of anomalies. The proposed IQA approach achieves significant improvements (>10%) in terms of Dice coefficient (DICE) and Area Under the Precision-Recall Curve (AUPRC) on the BraTS21 (T2, FLAIR) and MSULB datasets.
  arXiv  Detail & Related papers  (2024-08-15T15:55:07Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images [0.0]
  Breast cancer (BC) significantly contributes to cancer-related mortality in women. accurately distinguishing malignant mass lesions remains challenging. We propose a novel deep learning approach for BC screening utilizing mammography images.
  arXiv  Detail & Related papers  (2023-10-30T10:22:14Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification [2.4866930218890837]
  We propose a novel framework called Large Margin aware (LMF) loss to mitigate the class imbalance problem in medical imaging datasets. This framework harnesses the distinct characteristics of both loss functions by enforcing wider margins for minority classes. We provide empirical evidence that our proposed framework consistently outperforms other baseline methods.
  arXiv  Detail & Related papers  (2022-12-24T14:19:44Z)
• MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer [62.997667081978825]
  We propose the Multi-instance RST with a drop-max layer, namely MIRST-DM, to learn smoother decision boundaries on small datasets. The proposed approach was validated using a small breast ultrasound dataset with 1,190 images.
  arXiv  Detail & Related papers  (2022-05-02T20:25:26Z)
• Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities [60.079782224958414]
  We will present and discuss opportunities and challenges brought by a new deep learning method by AUC (aka underlinebf Deep underlinebf AUC classification)
  arXiv  Detail & Related papers  (2021-11-01T15:31:32Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network [8.950918531231158]
  This paper presents a deep learning-based framework, namely OsteoHRNet, that automatically assesses the Knee Osteoarthritis severity in terms of Kellgren and Lawrence grade classification from X-rays. Our proposed model has achieved the best multiclass accuracy of 71.74% and MAE of 0.311 on the baseline cohort of the OAI dataset.
  arXiv  Detail & Related papers  (2021-06-27T17:29:46Z)

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.