Multi-loss ensemble deep learning for chest X-ray classification
- URL: http://arxiv.org/abs/2109.14433v1
- Date: Wed, 29 Sep 2021 14:14:04 GMT
- Title: Multi-loss ensemble deep learning for chest X-ray classification
- Authors: Sivaramakrishnan Rajaraman, Ghada Zamzmi, Sameer Antani
- Abstract summary: Class imbalance is common in medical image classification tasks, where the number of abnormal samples is fewer than the number of normal samples.
We propose novel loss functions to train a DL model and analyze its performance in a multiclass classification setting.
- Score: 0.8594140167290096
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Class imbalance is common in medical image classification tasks, where the
number of abnormal samples is fewer than the number of normal samples. The
difficulty of imbalanced classification is compounded by other issues such as
the size and distribution of the dataset. Reliable training of deep neural
networks continues to be a major challenge in such class-imbalanced conditions.
The loss function used to train the deep neural networks highly impact the
performance of both balanced and imbalanced tasks. Currently, the cross-entropy
loss remains the de-facto loss function for balanced and imbalanced
classification tasks. This loss, however, asserts equal learning to all
classes, leading to the classification of most samples as the majority normal
class. To provide a critical analysis of different loss functions and identify
those suitable for class-imbalanced classification, we benchmark various
state-of-the-art loss functions and propose novel loss functions to train a DL
model and analyze its performance in a multiclass classification setting that
classifies pediatric chest X-rays as showing normal lungs, bacterial pneumonia,
or viral pneumonia manifestations. We also construct prediction-level and
model-level ensembles of the models that are trained with various loss
functions to improve classification performance. We performed localization
studies to interpret model behavior to ensure that the individual models and
their ensembles precisely learned the regions of interest showing disease
manifestations to classify the chest X-rays to their respective categories.
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) - Few-shot learning for COVID-19 Chest X-Ray Classification with
Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research.
Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images.
We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
arXiv Detail & Related papers (2024-01-18T16:59:27Z) - Unleashing the power of Neural Collapse for Transferability Estimation [42.09673383041276]
Well-trained models exhibit the phenomenon of Neural Collapse.
We propose a novel method termed Fair Collapse (FaCe) for transferability estimation.
FaCe yields state-of-the-art performance on different tasks including image classification, semantic segmentation, and text classification.
arXiv Detail & Related papers (2023-10-09T14:30:10Z) - Balanced Classification: A Unified Framework for Long-Tailed Object
Detection [74.94216414011326]
Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories.
We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution.
BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
arXiv Detail & Related papers (2023-08-04T09:11:07Z) - Imbalanced Nodes Classification for Graph Neural Networks Based on
Valuable Sample Mining [9.156427521259195]
A new loss function FD-Loss is reconstructed based on the traditional algorithm-level approach to the imbalance problem.
Our loss function can effectively solve the sample node imbalance problem and improve the classification accuracy by 4% compared to existing methods in the node classification task.
arXiv Detail & Related papers (2022-09-18T09:22:32Z) - Analyzing the Effects of Handling Data Imbalance on Learned Features
from Medical Images by Looking Into the Models [50.537859423741644]
Training a model on an imbalanced dataset can introduce unique challenges to the learning problem.
We look deeper into the internal units of neural networks to observe how handling data imbalance affects the learned features.
arXiv Detail & Related papers (2022-04-04T09:38:38Z) - 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) - The Effect of the Loss on Generalization: Empirical Study on Synthetic
Lung Nodule Data [13.376247652484274]
We show that different loss functions lead to different features being learned and consequently affect the generalization ability of the classifier on unseen data.
This study provides some important insights into the design of deep learning solutions for medical imaging tasks.
arXiv Detail & Related papers (2021-08-10T17:58:01Z) - Theoretical Insights Into Multiclass Classification: A High-dimensional
Asymptotic View [82.80085730891126]
We provide the first modernally precise analysis of linear multiclass classification.
Our analysis reveals that the classification accuracy is highly distribution-dependent.
The insights gained may pave the way for a precise understanding of other classification algorithms.
arXiv Detail & Related papers (2020-11-16T05:17:29Z) - An Aggregate Method for Thorax Diseases Classification [0.0]
We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem.
Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances.
arXiv Detail & Related papers (2020-08-07T06:36:07Z) - Multi-label Thoracic Disease Image Classification with Cross-Attention
Networks [65.37531731899837]
We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images.
We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
arXiv Detail & Related papers (2020-07-21T14:37:00Z)
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.