Self-supervised learning framework application for medical image analysis: a review and summary

The availability of huge annotated datasets and deep learning have led to astounding Natural Language Processing (NLP) progress. However, there is a plethora of low-resource languages, lack the labelled corpora to train the models in the supervision. This kind of digital divide does not allow fair NLP development across language groups. Self-supervised learning (SSL) is a new paradigm in recent years that leverages large amounts of untagged text to learn powerful representations with little or no manual annotation. This paper is a review of self-supervised deep learning models in low-resource NLP tasks. It begins with the definition of the principles of the SSL and the difference between this approach and supervised and unsupervised approaches. We describe why methods such as masked language modeling, contrastive learning, and autoregressive modeling underlie modern pre-trained transformers such as BERT, GPT, and mBERT. A particular interest is paid to multilingual and cross lingual SSL schemes which allow knowledge transfer between high-resource and low resource languages. Low-resource tasks, including machine translation, sentiment analysis, speech-to-text, and information retrieval are reviewed. As per benchmark studies, even in small labelled samples, the SSL models can achieve big gains in accuracy and generalization. Computation cost, representational bias and morphologically rich and under-documented language evaluation, are however, problematic. The researchers observe that the study suffers certain limitations, such as over-reliance on high-resource pretraining information, inequity between linguistic groups, and the difficulty of deploying large-scale SSL models in resource-constrained circumstances. The future directions include lightweight multilingual models, federation of learning in NLP and symbolic linguistic knowledge and combination with SSL. Self-supervised deep learning bridges an essential gap between the high-resource and low-resource languages, and would be a highly valuable step toward inclusive, global NLP innovation.

DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images

Spatiotemporal self-supervised predictive learning for atmospheric variable prediction via multi-group multi-attention

Supervised deep learning methods have produced state-of-the-art results with large labeled datasets. However, accessing large labeled datasets is difficult in medical image analysis because of a shortage of medical experts, expensive annotations, and privacy constraints in the healthcare domain. Self-supervised learning is a branch of machine learning that exploits unlabeled data to encourage network weights toward a valid latent representation of the data during a so-called pretext task. The features learned by the model while solving pretext tasks are transferred to a downstream task where limited annotations are available. In this work, we propose PatchLoc, a novel pretext task whose objective is to find the location of a given patch from an image as a source of supervision. We validated the effectiveness of PatchLoc on a downstream segmentation task using three different medical datasets. PatchLoc yields substantial improvements compared to U-Net trained from scratch and other pretext task-based approaches in a low data regime.

Deep learning has emerged as a powerful tool in various domains, revolutionising machine learning research. However, one persistent challenge is the scarcity of labelled training data, which hampers the performance and generalisation of deep learning models. To address this limitation, researchers have developed innovative methods to overcome data scarcity and enhance deep model learning capabilities. Two prevalent techniques that have gained significant attention are transfer learning and self-supervised learning. Transfer learning leverages knowledge learned from pre-training on a large-scale dataset, such as ImageNet, and applies it to a target task with limited labelled data. This approach allows models to benefit from the learned representations and effectively transfer knowledge to new tasks, resulting in improved learning performance and generalisation. On the other hand, self-supervised learning focuses on training models using pretext tasks that do not require manual annotation, allowing them to learn valuable representations from large amounts of unlabelled data. These learned representations can then be fine-tuned for downstream tasks, mitigating the need for extensive labelled data. In recent years, transfer and self-supervised learning have found applications in various fields, including medical image processing, video recognition, and natural language processing. These approaches have demonstrated remarkable achievements, enabling breakthroughs in areas such as disease diagnosis, object recognition, and language understanding. However, while these methods offer numerous advantages, they also have limitations. For example, transfer learning may face domain mismatch issues between the pre-training and target domains, while self-supervised learning requires careful design of pretext tasks to ensure meaningful representations. This review paper explores the recent applications of these pre-training methods in various fields within the past three years. It delves into the advantages and limitations of each approach, assesses the performance of models employing these techniques, and identifies potential directions for future research. By providing a comprehensive review of current pre-training methods, this article offers guidance for selecting the best technique for specific deep learning applications to address the data scarcity issue.

Self-supervised video representation learning leaves out heavy manual annotation by automatically excavating supervisory signals. Although contrastive learning based approaches exhibit superior performances, pretext task based approaches still deserve further study. This is because the pretext tasks exploit the nature of data and encourage feature extractors to learn spatiotemporal logic by discovering dependencies among video clips or cubes, without manual engineering on data augmentations or manual construction of contrastive pairs. To utilize chronological property more effectively and efficiently, this work proposes a novel pretext task, named serial restoration of shuffled clips (SRSC), disentangled by an elaborately designed task network composed of an order-aware encoder and a serial restoration decoder. In contrast to other order based pretext tasks that formulate clip order recognition as a one-step classification problem, the proposed SRSC task restores shuffled clips into the right order in multiple steps. Owing to the excellent elasticity of SRSC, a novel taxonomy of curriculum learning is further proposed to equip SRSC with different pre-training strategies. According to the factors that affect the complexity of solving the SRSC task, the proposed curriculum learning strategies can be categorized into task based, model based and data based. Extensive experiments are conducted on the subdivided strategies to explore their effectiveness and noteworthy laws. Compared with existing approaches, this work demonstrates that the proposed approach achieves state-of-the-art performances in pretext task based self-supervised video representation learning and a majority of the proposed strategies further boost the performance of downstream tasks. For the first time, the features pre-trained by the pretext tasks are applied to video captioning by feature-level early fusion, and enhance the input of existing approaches as a lightweight plugin.

Supervised learning technologies have been used in medical-data classification to improve diagnosis efficiency and reduce human diagnosis errors. A large amount of manually annotated data are required for the fully supervised learning process. However, annotating data information will consume a large amount of manpower and resources. Self-supervised learning has great advantages in solving this problem. Self-supervised learning mainly uses pretext tasks to mine its own supervised information from large-scale unsupervised data. And this constructed supervised information is used to train the network to learn valuable representations for downstream tasks. This study designs a general and efficient model for the diagnosis and classification of medical sensor data based on contrastive predictive coding (CPC) in self-supervised learning, called TCC, which consists of two steps. The first step is to design a pretext task based on the idea of CPC, which aims to extract effective features between different categories using its encoder. The second step designs a downstream classification task with lower time and space complexity to perform a supervised type of training using the features extracted by the encoder of the pretext task. Finally, to demonstrate the performance of the proposed framework in this paper, we compare the proposed framework with recent state-of-the-art works. Experiments comparing the proposed framework with supervised learning are also set up under the condition of different proportions of labeled data.

Whether tracking patient progress for clinical decision making, or investigating novel therapies, automated analysis of Computed Tomography (CT) imaging data is essential for the future of digital radiomics. In digital radiomics, as in all medical imaging, well annotated data is scarce, whereas unlabelled images are relatively plentiful. In other fields of image processing and medical imaging, the application of self-supervised learning (SSL) to large quantities of unlabelled data has resulted in great strides forward for fast, scalable, and interpretable image analysis. In this work we present a new approach applying SSL to CT imaging data which allows for: 1) Improved performance on image classification tasks, based on 2) dramatically reduced quantity of annotated CT imaging data, whilst also 3) enabling easy exploration and interpretation of the image regions. We applied a selection of self-supervised approaches (BYOL, DINO, SimCLR, & inpainting) to CT imaging data. Because the high dimensionality of CT data prevents us from using them directly to out-of the box SSL models, we adopt a 3D patching approach to reduce the dimensionality of the neural net input, and process each patch independently. We train our self-supervised models on public datasets (DeepLesion, NSCLC), and we specialize these models for tumor classification tasks that we evaluate on AstraZeneca sponsored clinical trials. The specialization is done in two different ways: 1) we use the pre-trained SSL model as an encoder that transforms the images of the clinical study into embeddings, on which we apply supervised classification models; 2) we use transfer learning to fine-tune supervised classification models that take the patches directly as inputs. We find that self-supervised pre-training significantly improves the accuracy on tumor classification tasks compared against a supervised learning baseline. Additionally, using the SSL embeddings we build an interactive map of CT imaging data enabling quick and intuitive inspection of the relevant regions. Our findings show that SSL constitutes an important tool for medical imaging analysis. SSL results in models that generalize better, and enable improved downstream interpretability and predictions. Furthermore, well trained SSL models can be re-applied to multiple indications because they are pre-trained on broad and diverse CT imaging data. Citation Format: Leon Fedden, Zhenning Zhang, Khan Baykaner, Qin Li, Lucas Bordeaux. DIME-CT: Self-supervised learning for medical image analysis using patch-based embeddings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1937.

Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches have been adopted using large-scale annotated datasets for histopathological image analysis. However, in several scenarios, the availability of large-scale annotated data is a bottleneck while training such models. Self-supervised learning (SSL) is an alternative paradigm that provides some respite by constructing models utilizing only the unannotated data which is often abundant. The basic idea of SSL is to train a network to perform one or many pseudo or pretext tasks on unannotated data and use it subsequently as the basis for a variety of downstream tasks. It is seen that the success of SSL depends critically on the considered pretext task. While there have been many efforts in designing pretext tasks for classification problems, there have not been many attempts on SSL for histopathological image segmentation. Motivated by this, we propose an SSL approach for segmenting histopathological images via generative diffusion models. Our method is based on the observation that diffusion models effectively solve an image-to-image translation task akin to a segmentation task. Hence, we propose generative diffusion as the pretext task for histopathological image segmentation. We also utilize a multi-loss function-based fine-tuning for the downstream task. We validate our method using several metrics on two publicly available datasets along with a newly proposed head and neck (HN) cancer dataset containing Hematoxylin and Eosin (H&E) stained images along with annotations.

Training deep learning models on medical images heavily depends on experts’ expensive and laborious manual labels. In addition, these images, labels, and even models themselves are not widely publicly accessible and suffer from various kinds of bias and imbalances. In this paper, chest X-ray pre-trained model via self-supervised contrastive learning (CheSS) was proposed to learn models with various representations in chest radiographs (CXRs). Our contribution is a publicly accessible pretrained model trained with a 4.8-M CXR dataset using self-supervised learning with a contrastive learning and its validation with various kinds of downstream tasks including classification on the 6-class diseases in internal dataset, diseases classification in CheXpert, bone suppression, and nodule generation. When compared to a scratch model, on the 6-class classification test dataset, we achieved 28.5% increase in accuracy. On the CheXpert dataset, we achieved 1.3% increase in mean area under the receiver operating characteristic curve on the full dataset and 11.4% increase only using 1% data in stress test manner. On bone suppression with perceptual loss, we achieved improvement in peak signal to noise ratio from 34.99 to 37.77, structural similarity index measure from 0.976 to 0.977, and root-square-mean error from 4.410 to 3.301 when compared to ImageNet pretrained model. Finally, on nodule generation, we achieved improvement in Fréchet inception distance from 24.06 to 17.07. Our study showed the decent transferability of CheSS weights. CheSS weights can help researchers overcome data imbalance, data shortage, and inaccessibility of medical image datasets. CheSS weight is available at https://github.com/mi2rl/CheSS.

In Self-Supervised Learning (SSL), various pretext tasks are designed for learning feature representations through contrastive loss. However, previous studies have shown that this loss is less tolerant to semantically similar samples due to the inherent defect of instance discrimination objectives, which may harm the quality of learned feature embeddings used in downstream tasks. To improve the discriminative ability of feature embeddings in SSL, we propose a new loss function called Angular Contrastive Loss (ACL), a linear combination of angular margin and contrastive loss. ACL improves contrastive learning by explicitly adding an angular margin between positive and negative augmented pairs in SSL. Experimental results show that using ACL for both supervised and unsupervised learning significantly improves performance. We validated our new loss function using the FSDnoisy18k dataset, where we achieved 73.6% and 77.1% accuracy in sound event classification using supervised and self-supervised learning, respectively.

The application of deep learning has resulted in significant advancements in computer vision systems; nonetheless, extensive labelled datasets and processing resources are required. In a lot of different domains, particularly emerging countries and edge-based deployments, there is a lack of labelled data, annotation is expensive, and computational resources is limited. An innovative Self-Supervised Learning (SSL) architecture has been developed as a result of this research. It is designed for low-resource computer vision applications that have limited labelled data and computational resources. Using unlabelled data, pretext tasks, representation consistency learning, and lightweight feature encoders, the proposed method is able to learn strong visual representations without the need for manual annotation. Under low-label regimes, the suggested method achieves better results than the usual supervised and transfer learning baselines when applied to benchmark datasets. The use of self-supervised learning makes it possible to create reliable computer vision systems in situations where resources are limited, and it is also scalable.

Deep learning methodologies have significantly advanced the fields of computer vision and machine learning, enhancing performance across various tasks like classification, regression, and detection. In remote sensing for Earth observation, deep neural networks have propelled state-of-the-art results. However, a major drawback is their dependence on large annotated datasets, necessitating extensive human effort, especially in specialized domains like medical imaging or remote sensing. To mitigate this reliance on annotations, several self-supervised representation learning techniques have emerged, aiming to learn unsupervised image representations applicable to downstream tasks such as image classification, object detection, or semantic segmentation. Consequently, self-supervised learning approaches have gained traction in remote sensing. This article surveys the foundational principles of various self-supervised methods, focusing on scene classification tasks. We elucidate key contributions, analyze experimental setups, and synthesize findings from each study. Furthermore, we conduct comprehensive experiments on two public scene classification datasets to evaluate and benchmark different self-supervised models

The image reconstruction process in medical imaging can be treated as solving an inverse problem. The inverse problem is usually solved using time-consuming iterative algorithms with sparsity or other constraints. Recently, deep neural network (DNN)-based methods have been developed to accelerate the inverse-problem-solving process. However, these methods typically adopt supervised learning scheme, which requires ground truths, or labels of the solutions, for training. In many applications, it would be challenging or even impossible to obtain the ground truth, such as the tissue reflectivity function in ultrasound beamforming. In this study, a general framework based on self-supervised learning (SSL) scheme is proposed to train a DNN to solve the inverse problems. In this way, the measurements can be used as both the inputs and the labels during the training of DNN. The proposed SSL method is applied to four typical linear inverse problems for validation, i.e., plane wave ultrasound and photoacoustic image reconstructions, compressed sensing-based synthetic transmit aperture dataset recovery and deconvolution in ultrasound localization microscopy. Results show that, using the proposed framework, the trained DNN can achieve improved reconstruction accuracy with reduced computational time, compared with conventional methods.

Deep learning methods have become an integral part of computer vision and machine learning research by providing significant improvement performed in many tasks such as classification, regression, and detection. These gains have been also observed in the field of remote sensing for Earth observation where most of the state-of-the-art results are now achieved by deep neural networks. However, one downside of these methods is the need for large amounts of annotated data, requiring lots of labor-intensive and expensive human efforts, in particular for specific domains that require expert knowledge such as medical imaging or remote sensing. In order to limit the requirement on data annotations, several self-supervised representation learning methods have been proposed to learn unsupervised image representations that can consequently serve for downstream tasks such as image classification, object detection or semantic segmentation. As a result, self-supervised learning approaches have been considerably adopted in the remote sensing domain within the last few years. In this article, we review the underlying principles developed by various self-supervised methods with a focus on scene classification task. We highlight the main contributions and analyze the experiments, as well as summarize the key conclusions, from each study. We then conduct extensive experiments on two public scene classification datasets to benchmark and evaluate different self-supervised models. Based on comparative results, we investigate the impact of individual augmentations when applied to remote sensing data as well as the use of self-supervised pre-training to boost the classification performance with limited number of labeled samples. We finally underline the current trends and challenges, as well as perspectives of self-supervised scene classification.