An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs

• URL: http://arxiv.org/abs/2402.05685v1
• Date: Thu, 8 Feb 2024 14:00:45 GMT
• Title: An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs
• Authors: Patrick Wienholt, Alexander Hermans, Firas Khader, Behrus Puladi, Bastian Leibe, Christiane Kuhl, Sven Nebelung, Daniel Truhn
• Abstract summary: We propose a framework that divides the ordinal regression problem into three parts: a model, a target function, and a classification function. We show that the choice of encoding has a strong impact on performance and that the best encoding depends on the chosen weighting of Cohen's kappa.
• Score: 50.285682227571996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regression problem into three parts: a model, a target function, and a classification function. Different encoding methods, including one-hot, Gaussian, progress-bar, and our soft-progress-bar, are applied using ResNet50 and ViT-B-16 deep learning models. We show that the choice of encoding has a strong impact on performance and that the best encoding depends on the chosen weighting of Cohen's kappa and also on the model architecture used. We make our code publicly available on GitHub.

Related papers

• EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models [70.60381055741391]
  Image restoration challenges related to illposed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning aims to address these deviations by combining the predictions of multiple base models. We employ an expectation (EM)-based algorithm to estimate ensemble weights for prediction candidates. Our algorithm is model-agnostic and training-free, allowing seamless integration and enhancement of various pre-trained image restoration models.
  arXiv  Detail & Related papers  (2024-10-30T12:16:35Z)
• Hardware Resilience Properties of Text-Guided Image Classifiers [15.787551066303804]
  We present a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors. Our approach achieves a remarkable $5.5times$ average increase in hardware reliability.
  arXiv  Detail & Related papers  (2023-11-23T15:38:13Z)
• Can we achieve robustness from data alone? [0.7366405857677227]
  Adversarial training and its variants have come to be the prevailing methods to achieve adversarially robust classification using neural networks. We devise a meta-learning method for robust classification, that optimize the dataset prior to its deployment in a principled way. Experiments on MNIST and CIFAR-10 demonstrate that the datasets we produce enjoy very high robustness against PGD attacks.
  arXiv  Detail & Related papers  (2022-07-24T12:14:48Z)
• Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks [76.16527095195893]
  We investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. We utilize Bayesian learning to train the model parameters, which is robust to overfitting.
  arXiv  Detail & Related papers  (2022-06-23T03:05:12Z)
• A Framework and Benchmark for Deep Batch Active Learning for Regression [2.093287944284448]
  We study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels, kernel transformations, and selection methods. Our proposed method outperforms the state-of-the-art on our benchmark, scales to large data sets, and works out-of-the-box without adjusting the network architecture or training code.
  arXiv  Detail & Related papers  (2022-03-17T16:11:36Z)
• Scaling Structured Inference with Randomization [64.18063627155128]
  We propose a family of dynamic programming (RDP) randomized for scaling structured models to tens of thousands of latent states. Our method is widely applicable to classical DP-based inference. It is also compatible with automatic differentiation so can be integrated with neural networks seamlessly.
  arXiv  Detail & Related papers  (2021-12-07T11:26:41Z)
• Few Shot Activity Recognition Using Variational Inference [9.371378627575883]
  We propose a novel variational inference based architectural framework (HF-AR) for few shot activity recognition. Our framework leverages volume-preserving Householder Flow to learn a flexible posterior distribution of the novel classes. This results in better performance as compared to state-of-the-art few shot approaches for human activity recognition.
  arXiv  Detail & Related papers  (2021-08-20T03:57:58Z)
• Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems [2.3322477552758234]
  We provide an in-depth analysis of how to tackle high cardinality categor-ical features with the quantile. Our proposal outperforms state-of-the-encoders, including the traditional statistical mean target encoder. We also describe how to expand the encoded values by creating a set of features with different quantiles.
  arXiv  Detail & Related papers  (2021-05-27T11:56:13Z)
• Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression [81.05772887221333]
  We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. We present a simple yet effective approach, named disentangled keypoint regression (DEKR) We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods.
  arXiv  Detail & Related papers  (2021-04-06T05:54:46Z)
• Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks [82.61182037130405]
  gradient descent (SGD) is the main approach for training deep networks. In this work, we compare Adam based variants based on the difference between the present and the past gradients. We have tested ensemble of networks and the fusion with ResNet50 trained with gradient descent.
  arXiv  Detail & Related papers  (2021-03-26T18:55:08Z)

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.