Imbalanced Classification in Medical Imaging via Regrouping

We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea is dramatically different from popular loss reweighting and class resampling methods. Our preliminary result on imbalanced medical image classification shows that this natural idea can substantially boost the classification performance as measured by average precision (approximately area-under-the-precision-recall-curve, or AUPRC), which is more appropriate for evaluating imbalanced classification than other metrics such as balanced accuracy

ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification

Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of information in tail classes. In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally. For information enhancement, we design a hybrid-proxy model to generate class-dependent proxies and propose a cycle update strategy for parameters optimization. A balanced-hybrid-proxy loss is designed to exploit relations between samples and proxies with different classes treated equally. Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into account, we further present a balanced-weighted cross-entropy loss following curriculum learning schedule. Experimental results on the classification of imbalanced skin lesion data have demonstrated the superiority and effectiveness of our method

Imbalance Learning and Its Application on Medical Datasets

To gain more valuable information from the increasing large amount of data, data mining has been a hot topic that attracts growing attention in this two decades. One of the challenges in data mining is imbalance learning, which refers to leaning from imbalanced datasets. The imbalanced datasets is dominated by some classes (majority) and other under-represented classes (minority). The imbalanced datasets degrade the learning ability of traditional methods, which are designed on the assumption that all classes are balanced and have equal misclassification costs, leading to the poor performance on the minority classes. This phenomenon is usually called the class imbalance problem. However, it is usually the minority classes of more interest and importance, such as sick cases in the medical dataset. Additionally, traditional methods are optimized to achieve maximum accuracy, which is not suitable for evaluating the performance on imbalanced datasets. From the view of data space, class imbalance could be classified as extrinsic imbalance and intrinsic imbalance. Extrinsic imbalance is caused by external factors, such as data transmission or data storage, while intrinsic imbalance means the dataset is inherently imbalanced due to its nature.  As extrinsic imbalance could be fixed by collecting more samples, this thesis mainly focus on on two scenarios of the intrinsic imbalance,  machine learning for imbalanced structured datasets and deep learning for imbalanced image datasets.  Normally, the solutions for the class imbalance problem are named as imbalance learning methods, which could be grouped into data-level methods (re-sampling), algorithm-level (re-weighting) methods and hybrid methods. Data-level methods modify the class distribution of the training dataset to create balanced training sets, and typical examples are over-sampling and under-sampling. Instead of modifying the data distribution, algorithm-level methods adjust the misclassification cost to alleviate the class imbalance problem, and one typical example is cost sensitive methods. Hybrid methods usually combine data-level methods and algorithm-level methods. However, existing imbalance learning methods encounter different kinds of problems. Over-sampling methods increase the minority samples to create balanced training sets, which might lead the trained model overfit to the minority class. Under-sampling methods create balanced training sets by discarding majority samples, which lead to the information loss and poor performance of the trained model. Cost-sensitive methods usually need assistance from domain expert to define the misclassification costs which are task specified. Thus, the generalization ability of cost-sensitive methods is poor. Especially, when it comes to the deep learning methods under class imbalance, re-sampling methods may introduce large computation cost and existing re-weighting methods could lead to poor performance. The object of this dissertation is to understand features difference under class imbalance, to improve the classification performance on structured datasets or image datasets. This thesis proposes two machine learning methods for imbalanced structured datasets and one deep learning method for imbalance image datasets. The proposed methods are evaluated on several medical datasets, which are intrinsically imbalanced.  Firstly, we study the feature difference between the majority class and the minority class of an imbalanced medical dataset, which is collected from a Chinese hospital. After data cleaning and structuring, we get 3292 kidney stone cases treated by Percutaneous Nephrolithonomy from 2012 to 2019. There are 651 (19.78% ) cases who have postoperative complications, which makes the complication prediction an imbalanced classification task. We propose a sampling-based method SMOTE-XGBoost and implement it to build a postoperative complication prediction model. Experimental results show that the proposed method outperforms classic machine learning methods. Furthermore, traditional prediction models of Percutaneous Nephrolithonomy are designed to predict the kidney stone status and overlook complication related features, which could degrade their prediction performance on complication prediction tasks. To this end, we merge more features into the proposed sampling-based method and further improve the classification performance. Overall, SMOTE-XGBoost achieves an AUC of 0.7077 which is 41.54% higher than that of S.T.O.N.E. nephrolithometry, a traditional prediction model of Percutaneous Nephrolithonomy. After reviewing the existing machine learning methods under class imbalance, we propose a novel ensemble learning approach called Multiple bAlance Subset Stacking (MASS). MASS first cuts the majority class into multiple subsets by the size of the minority set, and combines each majority subset with the minority set as one balanced subsets. In this way, MASS could overcome the problem of information loss because it does not discard any majority sample. Each balanced subset is used to train one base classifier. Then, the original dataset is feed to all the trained base classifiers, whose output are used to generate the stacking dataset. One stack model is trained by the staking dataset to get the optimal weights for the base classifiers. As the stacking dataset keeps the same labels as the original dataset, which could avoid the overfitting problem. Finally, we can get an ensembled strong model based on the trained base classifiers and the staking model. Extensive experimental results on three medical datasets show that MASS outperforms baseline methods.  The robustness of MASS is proved over implementing different base classifiers. We design a parallel version MASS to reduce the training time cost. The speedup analysis proves that Parallel MASS could reduce training time cost greatly when applied on large datasets. Specially, Parallel MASS reduces 101.8% training time compared with MASS at most in our experiments.  When it comes to the class imbalance problem of image datasets, existing imbalance learning methods suffer from the problem of large training cost and poor performance.  After introducing the problem of implementing resampling methods on image classification tasks, we demonstrate issues of re-weighting strategy using class frequencies through the experimental result on one medical image dataset.  We propose a novel re-weighting method Hardness Aware Dynamic loss to solve the class imbalance problem of image datasets. After each training epoch of deep neural networks, we compute the classification hardness of each class. We will assign higher class weights to the classes have large classification hardness values and vice versa in the next epoch. In this way, HAD could tune the weight of each sample in the loss function dynamically during the training process. The experimental results prove that HAD significantly outperforms the state-of-the-art methods. Moreover, HAD greatly improves the classification accuracies of minority classes while only making a small compromise of majority class accuracies. Especially, HAD loss improves 10.04% average precision compared with the best baseline, Focal loss, on the HAM10000 dataset. At last, I conclude this dissertation with our contributions to the imbalance learning, and provide an overview of potential directions for future research, which include extensions of the three proposed methods, development of task-specified algorithms, and fixing the challenges of within-class imbalance.2021-06-0

Class Instance Balanced Learning for Long-Tailed Classification

The long-tailed image classification task remains important in the development of deep neural networks as it explicitly deals with large imbalances in the class frequencies of the training data. While uncommon in engineered datasets, this imbalance is almost always present in real-world data. Previous approaches have shown that combining cross-entropy and contrastive learning can improve performance on the long-tailed task, but they do not explore the tradeoff between head and tail classes. We propose a novel class instance balanced loss (CIBL), which reweights the relative contributions of a cross-entropy and a contrastive loss as a function of the frequency of class instances in the training batch. This balancing favours the contrastive loss for more common classes, leading to a learned classifier with a more balanced performance across all class frequencies. Furthermore, increasing the relative weight on the contrastive head shifts performance from common (head) to rare (tail) classes, allowing the user to skew the performance towards these classes if desired. We also show that changing the linear classifier head with a cosine classifier yields a network that can be trained to similar performance in substantially fewer epochs. We obtain competitive results on both CIFAR-100-LT and ImageNet-LT.Comment: 8 pages, 2 figures, presented at the Conference on Robots and Vision 202

AraDIC : Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss

Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and handcrafted feature engineering. These could be eliminated by using character-level features. We propose a novel end-to-end Arabic document classification framework, Arabic document image-based classifier (AraDIC), inspired by the work on image-based character embeddings. AraDIC consists of an image-based character encoder and a classifier. They are trained in an end-to-end fashion using the class balanced loss to deal with the long-tailed data distribution problem. To evaluate the effectiveness of AraDIC, we created and published two datasets, the Arabic Wikipedia title (AWT) dataset and the Arabic poetry (AraP) dataset. To the best of our knowledge, this is the first image-based character embedding framework addressing the problem of Arabic text classification. We also present the first deep learning-based text classifier widely evaluated on modern standard Arabic, colloquial Arabic and classical Arabic. AraDIC shows performance improvement over classical and deep learning baselines by 12.29% and 23.05% for the micro and macro F-score, respectively