Only supervised training is compared with self-supervised training and fine-tuned versions of it.
\r\n\tAn update on clinical manifestations, their assessment, monitoring, and imagiology, including peripheral arthritis, enthesopathy, and extra-articular findings, and, the differential diagnosis with other diseases which evolves with axial and peripheral calcifications will be provided.
\r\n\r\n\t
\r\n\tAn important component of this book must be dedicated to the more recent treatments namely with biologic therapies but focusing also on new small molecule inhibitors and experimental therapies.
For an effective training, supervised learning requires a decent amount of labeled data, which is expensive. Unlabeled and inexpensive data (e.g. text and images on the Internet) is considerably more than the limited size datasets labeled by humans. We can use unlabeled data and perform a training on a pretext task, which is a
Although its origins date as back as 1990s [1, 2], contrastive learning has recently gained popularity due to its achievements in self-supervised learning, especially in computer vision. In
Self-supervised (left) vs. supervised (right) contrastive learning. Training results in an embedding space such that similar sample pairs stay close to each other while dissimilar ones are far apart. Figure is reproduced based on [
Since a self-supervised model does not know the actual labels corresponding to the inputs, its success depends on the design of the pretext tasks to generate the pseudo-labels from part of the input data. With these pseudo-labels, training on pretext task is performed with a ‘supervised’ loss function. Final performance on the pretext task is not important, but we hope that the learned intermediate representations can capture good information and be beneficial to a variety of downstream tasks.
Especially in computer vision and natural language processing (NLP), deep learning has become the most popular machine learning approach [5]. In parallel, self-supervised learning studies in computer vision have employed CNNs. Figure 2 shows the knowledge transfer from a self-supervised training to a supervised one in a deep learning setting. We save convolutional layers which are assumed to produce learned representations. We change/add fully connected layers, place a classifier head and train with the limited amount of labeled data for a downstream task like image classification or object detection.
A model is first trained with a pretext task with unlabeled data, then fine-tuned on the downstream task with limited amount of labeled data. Usually convolution layers, which are mostly responsible of learning representations, are transferred. A few fully-connected layers towards the end are changed or retrained.
The remainder of this chapter is structured as follows. Pretext tasks that are common in literature are reviewed in Section 2. Section 3 has detailed information about recent self-supervised learning models that use Siamese architectures. Section 4 provides our own experimental study where self-supervised contrastive learning is employed to learn representations of semantic segmentation masks, which is followed by the conclusions in Section 5.
Several random transformations applied to a patch from the unlabeled dataset to be used for self-supervised learning. Original sample is in top-left. The idea was first used by [
This is not only one of the first pretext tasks but also a very popular one. We will see in Section 3 that the mentioned type of augmentations have succeeded in learning useful representations and have achieved state-of-the-art results in transfer learning for downstream computer vision tasks.
Self-supervised representation learning by rotating input images, implemented in [
A model is trained to predict the colorized version of grayscale images (obtaining the dataset is inexpensive).
Last two pretext tasks (image colorization and inpainting) and some other GANs (e.g. image super-resolution [12]) are generation-based methods, where a missing info in the content is generated from available input. Whereas distortion, rotation and jigsaw are context-based self-supervision methods. For more detailed literature on pretext tasks, we refer the readers to the review in [13].
In our study, we concentrate on the context-based approach. Taking advantage of contrastive learning, this approach nowadays achieves state-of-the-art performance [14, 15, 16, 17]. We will go into details, especially the models with Siamese architecture, in Section 3. The generation-based and context-based method distinction also exists for video representation learning. In [18], an encoder network is used to learn video representations. Then, a decoder uses the representations to predict future frames. Differently, Qian
The rest of our chapter will consider works on image data. Before proceeding, let us give a few examples where contrastive learning is used for image-text pairs. Contrastive Language-Image Pre-training (CLIP, [20]) is a pretext task, where a text encoder and an image encoder are jointly trained to match captions with images. Training set consists of 400 million (image,text) pairs and an inter-modal contrastive loss is defined such that image and text embeddings of same objects will be closer to each other. Then, this pretraining is employed for a downstream task of zero-shot class prediction from images. Li
The goal of contrastive learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Implemented using Siamese networks, recent approaches create two different augmentations of samples and feed into the networks for contrastive learning. While SimCLR [14] and MoCo [15] use the negative samples directly along with the positive ones, BYOL [16] and SimSiam [17] achieved similar performance just with the positive samples. Differently, SwAV [23] forced consistency between cluster assignments of augmentations, instead of comparing features directly. Shortly after, vision transformers were included in self-supervised learning architectures [24, 25]. According to the results, not only image classification, but also object detection and semantic segmentation as downstream tasks benefit from self-supervised contrastive learning. Let us briefly explain some of these main approaches.
Let us describe SimCLR [14] first, then we will describe other methods by comparing to previous ones. SimCLR uses both positive and negative samples, but being positive or negative does not correspond to actual class labels. Augmented versions of the anchor are taken as positives, whereas samples belong to different instances are taken as negatives (Figure 6).
Let
A base feature encoder
Each representation
A batch of
SimCLR framework [
SimCLR uses the contrastive loss given in Eq. (1). This is a categorical cross-entropy loss to identify the positive sample among a set of negative samples (inspired from InfoNCE [27]).
A common protocol to evaluate self-supervised model efficiency is to place a linear classifier on top of (frozen) layers learnt by self-supervised training and train it for the downstream task with the labeled data. If the performance gap between this self-supervised encoder + linear classifier and a fully-supervised model is small, then the self-supervised training considered as efficient. An alternative evaluation protocol uses semi-supervised learning, i.e. pretrained network is re-trained as a whole with a certain percentage of available labels. Experiments reveal that re-training with only 10% of the labeled data achieves a performance (92.8%) very close to fully-supervised training performance on the whole dataset (94.2%) as reported in [14] (performances are top-5 classification accuracy on ImageNet dataset for ResNet-50).
Contrastive methods based on InfoNCE loss tend to work better with high number of negative examples since negative examples may represent underlying distribution more efficiently. SimCLR requires large batches (4096 samples) to ensure that there is enough negatives which demands high computation power (8 V100 GPUs in their study). To alleviate this need, MoCo [15] uses a dictionary of negative representations that is structured as a FIFO queue. This queue-based dictionary enables us to reuse representations of immediately preceding mini-batches of data. Thus, the main advantage of MoCo compared to SimCLR is that MoCo decouples the batch size from the number of negatives. SimCLR requires a large batch size and suffers performance drops when the batch size is reduced.
Given a query sample
MoCo framework [
Let us assume that there is a single positive key,
From the two encoders defined above, for
where
Later on, two design choices in SimCLR, namely MLP projection head and more stronger data augmentation were integrated into the approach resulting in MoCo-v2 [28].
Different from the approaches above, BYOL [16] achieves similar representation performance without using negative samples. It relies on two different neural networks (in contrast to SimCLR but similar to MoCo), referred to as online and target networks that interact. Online network has a predictor head. Target network has the same network architecture with the online network except for the predictor head (Figure 8). Parameters of the target network are not updated with back-propagation, but with a moving average of online network’s weights just as MoCo did for the momentum encoder.
Comparison of some Siamese architectures: SimCLR, BYOL and SimSiam. Dashed lines indicate back-propagation. Components colored in red are no more needed in SimSiam. Figure is reproduced based on [
It is curious that how the model escapes from collapsing (i.e. a trivial solution of fixed vector for each sample) when no negative samples are used. Authors of BYOL thought it is due to the momentum update, but later (with SimSiam [17]) it was discovered that using stop-gradient and predictor head is enough.
BYOL needs to maintain two copies of weights for the two separate networks which can be resource demanding. SimSiam [17] solves this problem with parameter sharing between the networks (with and w/o predictor head). The encoder
Finally, negative cosine similarity based total loss is computed in a symmetric fashion:
Figure 8 compares SimSiam with SimCLR and BYOL. SimSiam [17] does not use negative samples as SimCLR and MoCo did. Success with SimSiam also shows that momentum encoder (or any sort of moving average update of weights) is not needed. Stop-gradient operation and including predictor head are enough to prevent the model from collapsing.
SimSiam also presents transfer learning results for object detection and semantic segmentation downstream tasks. Results reveal that starting with a self-supervised pre-training on ImageNet outperforms image classification pre-training on ImageNet.
Caron
As a case study, we employ self-supervised contrastive learning to learn representations of semantically segmented images, i.e. semantic masks. This learning task is especially useful when two scenes are compared according to their semantic content. A use case would be image retrieval based localization, where standard approach extract features from RGB images and compare them to find the most similar image in the database [29, 30]. Recently, several studies showed that checking semantic resemblance between query and database images and using this similarity score while retrieving images improves localization accuracy [31, 32, 33]. The reason of improvement is that there is appearance difference between images taken at different times (query-database) due to illumination differences, viewpoint variations, seasonal changes. Although RGB image features are directly affected by those changes, semantic labels are stable most of the time (Figure 9).
The image on top-left was taken in 2008 and the image on top-right was taken in 2019 (source: Google street view) which respectively represent query and database for image retrieval. Observe illumination differences, viewpoint variations and changing objects. Bottom row shows their semantic segmentation results. Semantic similarity can help to verify/deny the localization result.
Given a semantic mask, obtaining the most similar result among the alternatives is not a trivial task. SIFT-like features do not exist to match. Moreover, two masks of the same scene are far from being identical not only because of changing content but also due to camera position and viewpoint variations. Thus, instead of employing a pixel-by-pixel label comparison score, a trainable semantic feature extractor is preferable.
Measuring semantic similarity to distinguish if two images belong to the same scene or not is a task especially suitable for self-supervised learning. Because datasets has to be prepared such that query and database are the same scene but different images (preferably long-term difference) is not easy. However, large amount of semantic masks can easily be obtained for a self-supervised training. We do not need groundtruth masks, since a successful estimation is enough to compute semantic similarity.
Our unsupervised learning dataset composed of 3484 images randomly taken from UCF dataset [34]. These are perspective images obtained from Google Street View panoramas which where taken in Pittsburgh, PA before 2014. Our supervised training and test datasets have query-database image pairs. Query images were also taken from UCF dataset (not coinciding with the 3484 images mentioned above). Database images were collected again from Google Street View panoramas at the same locations of query images but in 2019. This time gap results in seasonal changes and illumination variances. Also, a wide camera baseline between the database and query images conforms better to the long-term localization scenario [35]. Top row in Figure 9 shows an example of query-database image pair with time difference.
Since our aim to learn representations for semantic masks, we first automatically generated a semantic mask for each image in our dataset using a well-performing CNN model [36]. The CNN model we employed trained on Cityscapes [37], which is an urban scene understanding dataset consists of 30 visual classes. Examples are in Figure 9 (bottom row). After this point, we only have semantic masks in our dataset.
We used SimCLR [14] as our contrastive learning model and trained a ResNet-18 as the encoder. Encoder network (Figure 10) produces
Illustration of training a CNN model with self-supervised contrastive loss on a dataset that consists of semantically segmented masks. A positive pair is created from two randomly augmented views of the same mask, while negative pairs are created from views of other masks. All masks are encoded by the a shared encoder and projection heads before the representations are evaluated by the contrastive loss function.
CNN model, trained as explained above, is now ready to produce a similarity score when two semantic masks (one query and one database) are given. After self-supervised training, same network can be fine-tuned with a labeled dataset (query and database segmentation masks for the same scene). For this purpose, we prepared a dataset of 368 query images with their corresponding database images and extracted their semantic masks. Figure 9 shows an example of this preparation. Not surprisingly, this paired dataset is much smaller than the self-supervised training dataset. Here, common practice in literature is that the projection head (Figure 10) is removed after pretraining and a classifier head is added and trained with labeled data for the downstream task. However, our pretext and downstream tasks are the same. We learn semantic representations by treating each sample as its own class (exemplar-CNN [6], instance discrimination [7]). Thus, we do not place a classifier head, but we retrain the network (partially or full).
To be able to measure the capability of representing semantic masks, we conduct experiments that compare the retrieval accuracies of three training schemes. First is the CNN model which is trained with the supervised training set (368 query-database pairs). This is the baseline model that does not exploit self-supervised training at all. Second is the CNN model that is trained in a self-supervised fashion with 3484 individual semantic masks (no matching pairs). Lastly, the model with self-supervised training is retrained with the supervised training set. Two versions exist:
Trained models are tested on a test set which consists of 120 query-database pairs (different from 368 pairs used in training). Performances are compared with Recall@N metric. According to this metric, for a query image, the retrieval is considered successful if any of top-N retrieved database images is a correct match. In other words, Recall@1 is the recall when only the top-most retrieval is checked.
We observe in Table 1 that, only supervised training is not very successful. In fact, for certain N values self-supervised training managed to outperform supervised training alone. This shows the power of self-supervised learning when a large dataset is provided. Our unlabeled dataset is much larger than the labeled dataset (3484
Training methods | Retrieval accuracy (Recall@N) | ||||
---|---|---|---|---|---|
N = 1 | N = 2 | N = 3 | N = 4 | N = 5 | |
Only supervised training | 0.500 | 0.608 | 0.767 | 0.808 | 0.817 |
Only self-supervised training | 0.567 | 0.692 | 0.733 | 0.775 | 0.800 |
Dense layers were replaced and trained | 0.542 | 0.675 | 0.767 | 0.825 | 0.850 |
All layers were fine-tuned |
Only supervised training is compared with self-supervised training and fine-tuned versions of it.
Each row shows a retrieval result for a given query (left column). Examples show the cases where only supervised training (middle column) fails at Recall@1, but utilizing self-supervised training and then fine-tuning on the labeled dataset (query-database pairs) correctly retrieves (last column).
Table 2 presents the effect of minimum crop ratio parameter used in data augmentation module. Since it is an important parameter to represent the variation in our semantic masks, we compare the performance for minimum crop ratio from 0.9 to 0.1. Apart from individual Recall@N values, we also compute and plot mean recall (mean of all N values) in Table 2 last column and in Figure 12. We observe that it is highest around 0.6 and 0.7. Performance gradually drops as we increase or decrease the minimum crop ratio. A minimum random crop parameter of 0.6 means that cropped mask covers at least 60% area of the original mask. Since query and database masks in our training and test datasets have a considerable overlap ratio, it is reasonable that 0.6 or higher overlaps serve best. This result is also in accordance with the finding in [38] that there is a reverse U-shape relationship between the performance and the mutual information within augmented views. When crops are close to each other (high mutual information, e.g. crop ratio = 0.9) the model does not benefit from them much. On the other hand, for low crop ratios (low mutual information) model can not learn well since views look quite different from each other. Peak performance stays somewhere in between.
Crop ratio | Retrieval accuracy (Recall@N) | |||||
---|---|---|---|---|---|---|
N = 1 | N = 2 | N = 3 | N = 4 | N = 5 | mean | |
0.90 | 0.608 | 0.708 | 0.758 | 0.817 | 0.858 | 0.750 |
0.80 | 0.617 | 0.733 | 0.800 | 0.848 | 0.867 | 0.773 |
0.70 | 0.617 | 0.742 | 0.782 | |||
0.60 | 0.808 | 0.867 | ||||
0.50 | 0.617 | 0.700 | 0.767 | 0.808 | 0.833 | 0.745 |
0.40 | 0.575 | 0.692 | 0.717 | 0.783 | 0.825 | 0.718 |
0.30 | 0.567 | 0.675 | 0.742 | 0.767 | 0.808 | 0.712 |
0.20 | 0.542 | 0.633 | 0.717 | 0.767 | 0.783 | 0.688 |
0.10 | 0.525 | 0.608 | 0.675 | 0.742 | 0.783 | 0.667 |
Effect of the minimum crop ratio parameter in data augmentation at the stage of retraining of the self-supervised model.
Mean Recall@N values for varying min. Crop ratio parameter. Observe the reverse U-shape with a peak at 0.6.
In this chapter, we presented the main concepts in self-supervised contrastive learning and reviewed the approaches that attracted attention due to their success in computer vision. Contrastive learning that aims to end up in an embedding space where similar samples stay close to each other was implemented successfully with Siamese neural networks. Necessity on huge computation power was also alleviated with the most recent models. Currently, for common downstream tasks of computer vision such as object detection and semantic segmentation, self-supervised pre-training is a better alternative than using a model trained on ImageNet for image classification.
We also presented a case study where self-supervised contrastive learning is applied to learn representations of semantic masks of images. Performance was evaluated on an image retrieval task where the most similar semantic mask is retrieved from the database for a given query. In compliance with the results on other vision tasks in the literature, fine-tuning the self-supervised model with available labeled data gave better results than the supervised training alone.
This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant No. 120E500 and also under 2214-A International Researcher Fellowship Programme.
CNN | Convolutional neural network |
RGB | Red green blue |
NLP | Natural language processing |
LSTM | Long short term memory |
GAN | Generative adversarial network |
MoCo | Momentum contrast |
BYOL | Bootstrap your own latent |
MLP | Multi-layer perceptron |
FIFO | First in first out |
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Therefore, consistent openly accessible information with reproducibility may have significant role in regulating the major causes of mortality both in premature child and adults. The United Nations (UN) reported that 86% deaths (48 million deaths) out of 56 million globally deaths occurred in the LMICs in 2010. The major dilemma is that most of the deaths do not have a diagnosis of COD in such countries. Despite of the allocation of a large portion of resources to decrease the devastating impacts of chronic illnesses, their prevalence as well as the health and economic consequences remains staggeringly high. There are multiple levels of interventions that can help in bringing about significant and promising improvements in the healthcare system. Currently, Pakistan is facing double burden of malnutrition with record high prevalence rates of chronic diseases. Pakistan spends only a marginal of its GDP (1.2%) versus the recommended 5% by World Health Organization. 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This review focuses on the value of conventional histopathological characteristics including histological tumor subtype, Clark level, Breslow thickness, solar elastosis, ulceration, regression, lymphovascular invasion, mitotic counts, peritumoral lymphocyte infiltration, clinical characteristics such as age, gender, length of follow-up after surgery, recurrence, or metastasis, and progression-free survival, and tumor BRAF and NRAS mutations.",book:{id:"11594",title:"Melanoma - Standard of Care, Challenges, and Updates in Clinical Research",coverURL:"https://cdn.intechopen.com/books/images_new/11594.jpg"},signatures:"Tatjana Zablocka and Sergejs Isajevs"},{id:"82355",title:"Vein of Marshall Ethanol Infusion in Setting of Atrial Fibrillation Ablation",slug:"vein-of-marshall-ethanol-infusion-in-setting-of-atrial-fibrillation-ablation",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.105593",abstract:"Catheter ablation especially in persistent atrial fibrillation has limited success. Strategies beyond pulmonary veins isolation failed to demonstrate improvement of long-term rhythm maintenance. The vein of Marshall (VOM) is a promising therapeutic target as it fit perfectly with “Coumel’s triangle”: triggers in form of focal activities or stable reentries priming atrial fibrillation comes typically from tissue surrounding the VOM, it colocalize with mitral line especially in the epicardial part difficult to approach by endocardial ablations, it contains autonomic parasympathetic and sympathetic innervation implicated in arrhythmogenesis. Epicardial chemical ablation by ethanol delivery directly inside the vein of Marshall represents an attractive therapeutic approach eliminating arrhythmic triggers and autonomic modulators and, as it colocalize with the trajectory of the mitral isthmus, completing the integrity of that linear lesion. Based on advantages provided from VOM alcoholization, this technique has been progressively introduced in addiction to standard ablation strategies in atrial fibrillation treatment. This chapter aims to describe the electrophysiological characteristics of vein of Marshall, the technical aspects of ethanol delivery and the evidences from the literature supporting the emerging role of VOM alcoholization in atrial fibrillation treatment.",book:{id:"11655",title:"Atrial Fibrillation - Diagnosis and Management in the 21st Century",coverURL:"https://cdn.intechopen.com/books/images_new/11655.jpg"},signatures:"Andrea Rossi, Procolo Marchese and Marcello Piacenti"},{id:"82547",title:"Use of Melatonin as a Feed Additive",slug:"use-of-melatonin-as-a-feed-additive",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.105999",abstract:"Melatonin is a molecule that plays an active role in reducing many stress factors in plants and has important functions in the growth, development and reproduction of plants. It has many physiological functions that directly affect feed consumption, feed efficiency, energy metabolism and immune system in animal organisms. In addition, its anti-inflammatory, antioxidant, anticancer and antiapoptotic effects are also known. While melatonin has an antioxidative effect at low doses, it can exert a prooxidant effect at high doses. It has been suggested that when melatonin is used as a silage additive, it increases the total acid content of the silage and significantly improves the silage fermentation quality by lowering the pH level and butyric acid. Although it has positive effects on mammary gland involution and general health in ruminants, its effects on yield parameters have not been proven. Broilers and layers are expected high productivity and performance, in this regard, they are faced with stress factors such as intensive feeding and housing conditions. Considering its positive effects on stress factors, health and productivity, melatonin is a promising feed additive. Effects of melatonin additive or supplements on animal productivity and health should be revealed in further studies.",book:{id:"11707",title:"Melatonin - Recent Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11707.jpg"},signatures:"Oğuzhan Kahraman, Zekeriya Safa İnanç, Huzur Derya Arık and Mustafa Selçuk Alataş"},{id:"82535",title:"Advanced, Imageless Navigation in Contemporary THA: Optimising Acetabular Component Placement",slug:"advanced-imageless-navigation-in-contemporary-tha-optimising-acetabular-component-placement",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105493",abstract:"Total hip arthroplasty (THA) stands as a reliable and effective way to manage end-stage hip disease secondary to a number of aetiologic conditions. While target ‘safe zones’ are widely quoted and endorsed, an increasingly robust body of evidence suggests that such idealised implantation goals have limited utility in patient-to-patient considerations and that even with a precise goal in mind, surgeons perform inconsistently in achieving these targets intra-operatively. Inter-patient variability, the concept of ‘functional’ safe zones and the largely under-appreciated impact of poor patient positioning (and progressive loss of position during the case) are all recognised and evidence-supported opponents of conventional ‘40/15’ approaches. In an environment whereby accountable cost utility, maximised surgical consistency (i.e., outlier minimisation), improved attainment of target position, and awareness of the radiation exposure burden of many pre-operative templating regimes are all paramount, there appears to be an increasing role for the application of imageless ‘mini’ intra-operative navigation systems for primary (and revision) THA procedures. This chapter reviews the evolution of THA navigation and discusses contemporary applications, defines the challenges associated with unanticipated pelvic movement, and explores potential future directions in the use of this exciting technology.",book:{id:"11873",title:"Arthroplasty - Advanced Techniques and Future Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11873.jpg"},signatures:"Andrew P. 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