Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification

• URL: http://arxiv.org/abs/2307.15987v1
• Date: Sat, 29 Jul 2023 13:38:19 GMT
• Title: Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification
• Authors: Zhongzheng Huang, Jiawei Wu, Tao Wang, Zuoyong Li, Anastasia Ioannou
• Abstract summary: Class-Specific Distribution Alignment (CSDA) is a semi-supervised learning framework based on self-training. We show that our method provides competitive performance on semi-supervised skin disease, thoracic disease, and endoscopic image classification tasks.
• Score: 14.343079589464994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases. To address this problem, we propose Class-Specific Distribution Alignment (CSDA), a semi-supervised learning framework based on self-training that is suitable to learn from highly imbalanced datasets. Specifically, we first provide a new perspective to distribution alignment by considering the process as a change of basis in the vector space spanned by marginal predictions, and then derive CSDA to capture class-dependent marginal predictions on both labeled and unlabeled data, in order to avoid the bias towards majority classes. Furthermore, we propose a Variable Condition Queue (VCQ) module to maintain a proportionately balanced number of unlabeled samples for each class. Experiments on three public datasets HAM10000, CheXpert and Kvasir show that our method provides competitive performance on semi-supervised skin disease, thoracic disease, and endoscopic image classification tasks.

Related papers

• Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification [29.730360798234294]
  We introduce an Iterative Online Image Synthesis framework to address the class imbalance problem in medical image classification. Our framework incorporates two key modules, namely Online Image Synthesis (OIS) and Accuracy Adaptive Sampling (AAS) To evaluate the effectiveness of our proposed method in addressing imbalanced classification, we conduct experiments on the HAM10000 and APTOS datasets.
  arXiv  Detail & Related papers  (2024-03-13T10:51:18Z)
• Twice Class Bias Correction for Imbalanced Semi-Supervised Learning [59.90429949214134]
  We introduce a novel approach called textbfTwice textbfClass textbfBias textbfCorrection (textbfTCBC) We estimate the class bias of the model parameters during the training process. We apply a secondary correction to the model's pseudo-labels for unlabeled samples.
  arXiv  Detail & Related papers  (2023-12-27T15:06:36Z)
• SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification [11.435826510575879]
  Semi-supervised learning (SSL) methods can mitigate challenges by leveraging both labeled and unlabeled data. SSL methods for medical image classification need to address two key challenges: (1) estimating reliable pseudo-labels for the images in the unlabeled dataset and (2) reducing biases caused by class imbalance. In this paper, we propose a novel SSL approach, SPLAL, that effectively addresses these challenges.
  arXiv  Detail & Related papers  (2023-07-10T14:53:24Z)
• Class-Balancing Diffusion Models [57.38599989220613]
  Class-Balancing Diffusion Models (CBDM) are trained with a distribution adjustment regularizer as a solution. Our method benchmarked the generation results on CIFAR100/CIFAR100LT dataset and shows outstanding performance on the downstream recognition task.
  arXiv  Detail & Related papers  (2023-04-30T20:00:14Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation [11.21648118505577]
  Semi-supervised learning (SSL) is highly desirable for training with insufficient labeled data. We present a novel framework for barely-supervised knee segmentation with noisy and imbalanced labels. Our method outperforms the state-of-the-art SSL methods.
  arXiv  Detail & Related papers  (2022-05-07T12:53:06Z)
• 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)
• Semi-supervised learning for medical image classification using imbalanced training data [11.87832944550453]
  We propose Adaptive Blended Consistency Loss (ABCL) as a drop-in replacement for consistency loss in perturbation-based SSL methods. ABCL counteracts data skew by adaptively mixing the target class distribution of the consistency loss in accordance with class frequency. Our experiments with ABCL reveal improvements to unweighted average recall on two different imbalanced medical image classification datasets.
  arXiv  Detail & Related papers  (2021-08-20T01:06:42Z)
• RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data [47.00510780034136]
  Class imbalance is a familiar issue in the field of disease prediction. In this paper, we propose Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to enhance the performance of the graph-based classifier. We show the superiority of RA-GCN on synthetic and three publicly available medical datasets compared to the recent method.
  arXiv  Detail & Related papers  (2021-02-27T14:06:27Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.