Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

• URL: http://arxiv.org/abs/2112.11700v1
• Date: Wed, 22 Dec 2021 07:13:02 GMT
• Title: Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment
• Authors: Weihang Dai, Xiaomeng Li, Wan Hang Keith Chiu, Michael D. Kuo, Kwang-Ting Cheng
• Abstract summary: We propose the first contrastive learning framework for deep image regression, namely AdaCon. AdaCon consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. We demonstrate the effectiveness of AdaCon on two medical image regression tasks.
• Score: 22.717658723840255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, ie, bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.

Related papers

• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss [0.0]
  We introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases.
  arXiv  Detail & Related papers  (2022-11-22T17:05:30Z)
• Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI [1.419070105368302]
  We consider two pre-training approaches for driving a deep learning model to learn different representations. The effect of pretraining techniques is evaluated in two downstream segmentation applications using Magnetic Resonance (MR) images. We observed that DL models pretrained using self-supervision can be finetuned for comparable performance with fewer labeled datasets.
  arXiv  Detail & Related papers  (2022-05-26T02:23:14Z)
• Poseur: Direct Human Pose Regression with Transformers [119.79232258661995]
  We propose a direct, regression-based approach to 2D human pose estimation from single images. Our framework is end-to-end differentiable, and naturally learns to exploit the dependencies between keypoints. Ours is the first regression-based approach to perform favorably compared to the best heatmap-based pose estimation methods.
  arXiv  Detail & Related papers  (2022-01-19T04:31:57Z)
• 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)
• Human Pose Regression with Residual Log-likelihood Estimation [48.30425850653223]
  We propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution. RLE learns the change of the distribution instead of the unreferenced underlying distribution to facilitate the training process. Compared to the conventional regression paradigm, regression with RLE bring 12.4 mAP improvement on MSCOCO without any test-time overhead.
  arXiv  Detail & Related papers  (2021-07-23T15:06:31Z)
• About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations [2.3204178451683264]
  Variance Aware Training (VAT) method exploits this property by introducing the variance error into the model loss function. We validate VAT on three medical imaging datasets from diverse domains and various learning objectives.
  arXiv  Detail & Related papers  (2021-05-28T21:34:04Z)
• Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates [68.09049111171862]
  This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. We formulate the regression-free model updates into a constrained optimization problem. We empirically analyze how model ensemble reduces regression.
  arXiv  Detail & Related papers  (2021-05-07T03:33:00Z)
• Adversarial Regression Learning for Bone Age Estimation [6.942003070153651]
  We propose an adversarial regression learning network (ARLNet) for bone age estimation. Specifically, we first extract bone features from a fine-tuned Inception V3 neural network. We then propose adversarial regression loss and feature reconstruction loss to guarantee the transition from training data to test data.
  arXiv  Detail & Related papers  (2021-03-10T15:58:26Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.