The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis

Apprentissage d'espaces sémantiques

Dans cette dissertation, nous présentons plusieurs techniques d’apprentissage d’espaces sémantiques pour plusieurs domaines, par exemple des mots et des images, mais aussi à l’intersection de différents domaines. Un espace de représentation est appelé sémantique si des entités jugées similaires par un être humain, ont leur similarité préservée dans cet espace. La première publication présente un enchaînement de méthodes d’apprentissage incluant plusieurs techniques d’apprentissage non supervisé qui nous a permis de remporter la compétition “Unsupervised and Transfer Learning Challenge” en 2011. Le deuxième article présente une manière d’extraire de l’information à partir d’un contexte structuré (177 détecteurs d’objets à différentes positions et échelles). On montrera que l’utilisation de la structure des données combinée à un apprentissage non supervisé permet de réduire la dimensionnalité de 97% tout en améliorant les performances de reconnaissance de scènes de +5% à +11% selon l’ensemble de données. Dans le troisième travail, on s’intéresse à la structure apprise par les réseaux de neurones profonds utilisés dans les deux précédentes publications. Plusieurs hypothèses sont présentées et testées expérimentalement montrant que l’espace appris a de meilleures propriétés de mixage (facilitant l’exploration de différentes classes durant le processus d’échantillonnage). Pour la quatrième publication, on s’intéresse à résoudre un problème d’analyse syntaxique et sémantique avec des réseaux de neurones récurrents appris sur des fenêtres de contexte de mots. Dans notre cinquième travail, nous proposons une façon d’effectuer de la recherche d’image ”augmentée” en apprenant un espace sémantique joint où une recherche d’image contenant un objet retournerait aussi des images des parties de l’objet, par exemple une recherche retournant des images de ”voiture” retournerait aussi des images de ”pare-brises”, ”coffres”, ”roues” en plus des images initiales.In this work, we focus on learning semantic spaces for multiple domains, but also at the intersection of different domains. The semantic space is where the learned representation lives. This space is called semantic if similar entities from a human perspective have their similarity preserved in this space. We use different machine learning algorithms to learn representations with interesting intrinsic properties. The first article presents a pipeline including many different unsupervised learning techniques used to win the Unsupervised and Transfer Learning Challenge in 2011. In the second article, we present a pipeline taking advantage of the structure of the data for a scene classification problem. This approach allows us to drastically reduce the dimensionality while improving significantly on the scene recognition accuracy. The third article focuses on the space structure learned by deep representations. We show that performing the sampling procedure from deeper levels of representation space explores more of the different classes. In the fourth article, we tackle a semantic parsing problem with several Recurrent Neural Network architectures taking as input context windows of word embeddings. In the fifth article, an investigation on learning a single semantic space at the intersection of words and images is presented. We propose a way to perform ”augmented search” where a search on an image containing an object would also return images of the object’s parts

엣지 장비를 위한 한정된 데이터를 가지는 딥러닝 비전 어플리케이션의 빠른 적응

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022.2. 유승주.딥 러닝 기반 방법의 놀라운 성공은 주로 많은 양의 분류된 데이터로 달성되었다. 전통적인 기계 학습 방법과 비교해서 딥러닝 방법은 아주 큰 데이터셋으로부터 좋은 성능을 가진 모델을 학습할 수 있다. 하지만 고품질의 분류된 데이터는 만들기 어렵고 프라이버시 문제로 만들 수 없을 때도 있다. 게다가 사람은 아주 큰 분류된 데이터가 없어도 훌륭한 일반화 능력을 보여준다. 엣지 장비는 서버와 비교해서 제한적인 계산 능력을 가진다. 특히 학습 과정을 엣지 장비에서 수행하는 것은 매우 어렵다. 하지만, 도메인 변화 문제와 프라이버시 문제를 고려했을 때 엣지 장비에서 학습 과정을 수행하는 것은 바람직하다. 본 논문에서는 계산능력이 작은 엣지 장비를 위해 적응 과정을 전통적인 학습 과정 대신 고려한다. 전통적인 분류 문제는 학습 데이터와 테스트 데이터가 동일한 분포에서 파생되었음과 많은 양의 학습 데이터를 가정한다. 비지도 도메인 어댑테이션은 테스트 데이터가 학습데이터와 다른 분포에서 파생되는 상황을 가정하며 기존의 분류된 데이터와 학습된 모델을 이용해 새로운 데이터를 분류하는 문제이다. 퓨샷 학습은 적은 양의 학습 데이터를 가정하며 소수의 분류된 데이터만을 가지고 새로운 데이터를 분류하는 문제이다. 엣지 장비를 위해 이미지넷에서 미리 학습된 모델을 통해 비지도 도메인 어댑테이션 성능을 강화하는 방법과 지도 컨트라스티브 학습을 통해 퓨샷 학습 성능을 강화하는 방법을 제안하였다. 두 방법은 모두 적은 분류된 데이터 문제를 다루며 다만 서로 다른 시나리오를 가정한다. 첫 번째 방법은 엣지 장비를 위해 네트워크 모델과 파라미터 선택의 동시 최적화를 통해 비지도 도메인 어댑테이션 성능을 강화하는 방법이다. 이미지넷에서 미리 학습된 모델은 Office 데이터셋과 같이 작은 데이터셋을 다룰때 매우 중요하다. 특징 추출기를 갱신하지 않는 비지도 도메인 어댑테이션 알고리즘을 사용하고 아주 큰 이미지넷에서 미리 학습된 모델을 조합하는 방법으로 높은 정확도를 얻을 수 있다. 더 나아가 엣지 장비를 위해 작고 가벼운 이미지넷에서 미리 학습된 모델을 실험하였다. 지연시간을 줄이기 위해 예측기를 도입한 진화 알고리즘으로 방법의 시작부터 끝까지 최적화하였다. 그리고 프라이버시를 지키기 위한 비지도 도메인 어댑테이션 시나리오에 대해 고려하였다. 또한 엣지 장비에서 좀 더 현실적인 시나리오인 작은 데이터셋과 object detection 에 대해서도 실험하였다. 마지막으로 연속적인 데이터가 입력될 때 중간 데이터를 활용하여 지연시간을 더 감소시키는 방법을 실험하였다. Office31과 Office-Home 데이터셋에 대해 각각 5.99배와 9.06배 지연시간 감소를 달성하였다. 두 번째 방법은 지도 컨트라스티브 학습을 통해 퓨샷 학습 성능을 강화하는 방법이다. 퓨샷 학습 벤치마크에서는 베이스 데이터셋으로 특징 추출기를 학습하기 때문에 이미지넷에서 미리 학습된 모델을 사용할 수 없다. 대신에, 지도 컨트라스티브 학습을 통해 특징 추출기를 강화한다. 지도 컨트라스티브 학습과 정보 최대화 그리고 프로토타입 추정 방법을 조합하여 아주 높은 정확도를 얻을 수 있다. 특징 추출기와 미리 끝내기를 통해 이렇게 얻은 정확도를 수행시간 감소로 바꿀 수 있다. 트랜스덕티브 5-웨이 5-샷 학습 시나리오에서 3.87배 지연시간 감소를 달성하였다. 본 방법은 정확도를 증가시킨 후 지연시간을 감소시키는 방법으로 요약할 수 있다. 먼저 이미지넷에서 미리 학습된 모델을 쓰거나 지도 컨트라스티브 학습을 통해 특징 추출기를 강화해서 높은 정확도를 얻는다. 그 후 진화 알고리즘을 통해 시작부터 끝까지 최적화하거나 미리 끝내기를 통해 지연시간을 줄인다. 정확도를 증가시킨 후 지연시간을 감소시키는 두 단계 접근 방식은 엣지 장비를 위한 한정된 데이터를 가지는 딥러닝 비전 어플리케이션의 빠른 적응을 달성하는데 충분하다.The remarkable success of deep learning-based methods are mainly accomplished by a large amount of labeled data. Compared to conventional machine learning methods, deep learning-based methods are able to learn high quality model with a large dataset size. However, high-quality labeled data is expensive to obtain and sometimes preparing a large dataset is impossible due to privacy concern. Furthermore, human shows outstanding generalization performance without a huge amount of labeled data. Edge devices have a limited capability in computation compared to servers. Especially, it is challenging to implement training on edge devices. However, training on edge device is desirable when considering domain-shift problem and privacy concern. In this dissertation, I consider adaptation process as a conventional training counterpart for low computation capability edge device. Conventional classification assumes that training data and test data are drawn from the same distribution and training dataset is large. Unsupervised domain adaptation addresses the problem when training data and test data are drawn from different distribution and it is a problem to label target domain data using already existing labeled data and models. Few-shot learning assumes small training dataset and it is a task to predict new data based on only a few labeled data. I present 1) co-optimization of backbone network and parameter selection in unsupervised domain adaptation for edge device and 2) augmenting few-shot learning with supervised contrastive learning. Both methods are targeting low labeled data regime but different scenarios. The first method is to boost unsupervised domain adaptation by co-optimization of backbone network and parameter selection for edge device. Pre-trained ImageNet models are crucial when dealing with small dataset such as Office datasets. By using unsupervised domain adaptation algorithm that does not update feature extractor, large and powerful pre-trained ImageNet models can be used to boost the accuracy. We report state-of-the-art accuracy result with the method. Moreover, we conduct an experiment to use small and lightweight pre-trained ImageNet models for edge device. Co-optimization is performed to reduce the total latency by using predictor-guided evolutionary search. We also consider pre-extraction of source feature. We conduct more realistic scenario for edge device such as smaller target domain data and object detection. Lastly, We conduct an experiment to utilize intermediate domain data to reduce the algorithm latency further. We achieve 5.99x and 9.06x latency reduction on Office31 and Office-Home dataset, respectively. The second method is to augment few-shot learning with supervised contrastive learning. We cannot use pre-trained ImageNet model in the few-shot learning benchmark scenario as they provide base dataset to train the feature extractor from scratch. Instead, we augment the feature extractor with supervised contrastive learning method. Combining supervised contrastive learning with information maximization and prototype estimation technique, we report state-of-the-art accuracy result with the method. Then, we translate the accuracy gain to total runtime reduction by changing the feature extractor and early stopping. We achieve 3.87x latency reduction for transductive 5-way 5-shot learning scenarios. Our approach can be summarized as boosting the accuracy followed by latency reduction. We first upgrade the feature extractor by using more advanced pre-trained ImageNet model or by supervised contrastive learning to achieve state-of-the-art accuracy. Then, we optimize the method end-to-end with evolutionary search or early stopping to reduce the latency. Our two stage approach which consists of accuracy boosting and latency reduction is sufficient to achieve fast adaptation of deep learning vision applications with limited data for edge device.1. Introduction 1 2. Background 7 2.1 Dataset Size for Vision Applications 7 2.2 ImageNet Pre-trained Models 9 2.3 Augmentation Methods for ImageNet 12 2.4 Contrastive Learning 14 3. Problem Definitions and Solutions Overview 17 3.1 Problem Definitions 17 3.1.1 Unsupervised Domain Adaptation 17 3.1.2 Few-shot learning 18 3.2 Solutions overview 19 3.2.1 Co-optimization of Backbone Network and Parameter Selection in Unsupervised Domain Adaptation for Edge Device 20 3.2.2 Augmenting Few-Shot Learning with Supervised Contrastive Learning 21 4. Co-optimization of Backbone Network and Parameter Selection in Unsupervised Domain Adaptation for Edge Device 22 4.1 Introduction 23 4.2 Related Works 28 4.3 Methodology 33 4.3.1 Examining an Unsupervised Domain Adaptation Method 33 4.3.2 Boosting Accuracy with Pre-Trained ImageNet Models 36 4.3.3 Boosting Accuracy for Edge Device 38 4.3.4 Co-optimization of Backbone Network and Parameter Selection 39 4.4 Experiments 41 4.4.1 ImageNet and Unsupervised Domain Adaptation Accuracy 43 4.4.2 Accuracy with Once-For-All Network 52 4.4.3 Comparison with State-of-the-Art Results 58 4.4.4 Co-optimization for Edge Device 59 4.4.5 Pre-extraction of Source Feature 72 4.4.6 Results for Small Target Data Scenario 77 4.4.7 Results for Object Detection 78 4.4.8 Results for Classifier Fitting Using Intermediate Domain 80 4.4.9 Summary 81 4.5 Conclusion 84 5. Augmenting Few-Shot Learning with Supervised Contrastive Learning 85 5.1 Introduction 86 5.2 Related Works 89 5.3 Methodology 92 5.3.1 Examining A Few-shot Learning Method 92 5.3.2 Augmenting Few-shot Learning with Supervised Contrastive Learning 94 5.4 Experiments 97 5.4.1 Comparison to the State-of-the-Art 99 5.4.2 Ablation Study 102 5.4.3 Domain-Shift 105 5.4.4 Increasing the Number of Ways 106 5.4.5 Runtime Analysis 107 5.4.6 Limitations 109 5.5 Conclusion 110 6. Conclusion 111박

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

Agnostic Bayes

Tableau d'honneur de la Faculté des études supérieures et postdorales, 2014-2015L’apprentissage automatique correspond à la science de l’apprentissage à partir d’exemples. Des algorithmes basés sur cette approche sont aujourd’hui omniprésents. Bien qu’il y ait eu un progrès significatif, ce domaine présente des défis importants. Par exemple, simplement sélectionner la fonction qui correspond le mieux aux données observées n’offre aucune garantie statistiques sur les exemples qui n’ont pas encore été observées. Quelques théories sur l’apprentissage automatique offrent des façons d’aborder ce problème. Parmi ceux-ci, nous présentons la modélisation bayésienne de l’apprentissage automatique et l’approche PACbayésienne pour l’apprentissage automatique dans une vue unifiée pour mettre en évidence d’importantes similarités. Le résultat de cette analyse suggère que de considérer les réponses de l’ensemble des modèles plutôt qu’un seul correspond à un des éléments-clés pour obtenir une bonne performance de généralisation. Malheureusement, cette approche vient avec un coût de calcul élevé, et trouver de bonnes approximations est un sujet de recherche actif. Dans cette thèse, nous présentons une approche novatrice qui peut être appliquée avec un faible coût de calcul sur un large éventail de configurations d’apprentissage automatique. Pour atteindre cet objectif, nous appliquons la théorie de Bayes d’une manière différente de ce qui est conventionnellement fait pour l’apprentissage automatique. Spécifiquement, au lieu de chercher le vrai modèle à l’origine des données observées, nous cherchons le meilleur modèle selon une métrique donnée. Même si cette différence semble subtile, dans cette approche, nous ne faisons pas la supposition que le vrai modèle appartient à l’ensemble de modèles explorés. Par conséquent, nous disons que nous sommes agnostiques. Plusieurs expérimentations montrent un gain de généralisation significatif en utilisant cette approche d’ensemble de modèles durant la phase de validation croisée. De plus, cet algorithme est simple à programmer et n’ajoute pas un coût de calcul significatif à la recherche d’hyperparamètres conventionnels. Finalement, cet outil probabiliste peut également être utilisé comme un test statistique pour évaluer la qualité des algorithmes sur plusieurs ensembles de données d’apprentissage.Machine learning is the science of learning from examples. Algorithms based on this approach are now ubiquitous. While there has been significant progress, this field presents important challenges. Namely, simply selecting the function that best fits the observed data was shown to have no statistical guarantee on the examples that have not yet been observed. There are a few learning theories that suggest how to address this problem. Among these, we present the Bayesian modeling of machine learning and the PAC-Bayesian approach to machine learning in a unified view to highlight important similarities. The outcome of this analysis suggests that model averaging is one of the key elements to obtain a good generalization performance. Specifically, one should perform predictions based on the outcome of every model instead of simply the one that best fits the observed data. Unfortunately, this approach comes with a high computational cost problem, and finding good approximations is the subject of active research. In this thesis, we present an innovative approach that can be applied with a low computational cost on a wide range of machine learning setups. In order to achieve this, we apply the Bayes’ theory in a different way than what is conventionally done for machine learning. Specifically, instead of searching for the true model at the origin of the observed data, we search for the best model according to a given metric. While the difference seems subtle, in this approach, we do not assume that the true model belongs to the set of explored model. Hence, we say that we are agnostic. An extensive experimental setup shows a significant generalization performance gain when using this model averaging approach during the cross-validation phase. Moreover, this simple algorithm does not add a significant computational cost to the conventional search of hyperparameters. Finally, this probabilistic tool can also be used as a statistical significance test to evaluate the quality of learning algorithms on multiple datasets

Visual Tracking: An Experimental Survey

There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is difficult problem, therefore it remains a most active area of research in Computer Vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers

Model-driven and Data-driven Methods for Recognizing Compositional Interactions from Videos

The ability to accurately understand how humans interact with their surroundings is critical for many vision based intelligent systems. Compared to simple atomic actions (eg. raise hand), many interactions found in our daily lives are defined as a composition of an atomic action with a variety of arguments (eg. pick up a pen). Despite recent progress in the literature, there still remains fundamental challenges unique to recognizing interactions from videos. First, most of the action recognition literature assumes a problem setting where a pre-defined set of action labels is supported by a large and relatively balanced set of training examples for those labels. There are many realistic cases where this data assumption breaks down, either because the application demands fine-grained classification of a potentially combinatorial number of activities, and/or because the problem at hand is an “open-set” problem where new labels may be defined at test time. Second, many deep video models often simply represent video as a three-dimensional tensor and ignore the differences in spatial and temporal dimensions during the representation learning stage. As a result, data-driven bottom-up action models frequently over-fit to the static content of the video and fail to accurately capture the dynamic changes in relations among actors in the video. In this dissertation, we address the aforementioned challenges of recognizing fine-grained interactions from videos by developing solutions that explicitly represent interactions as compositions of simpler static and dynamic elements. By exploiting the power of composition, our ``detection by description'' framework expresses a very rich space of interactions using only a small set of static visual attributes and a few dynamic patterns. A definition of an interaction is constructed on the fly from first-principles state machines which leverage bottom-up deep-learned components such as object detectors. Compared to existing model-driven methods for video understanding, we introduce the notion of dynamic action signatures which allows a practitioner to express the expected temporal behavior of various elements of an interaction. We show that our model-driven approach using dynamic action signatures outperforms other zero-shot methods on multiple public action classification benchmarks and even some fully supervised baselines under realistic problem settings. Next, we extend our approach to a setting where the static and dynamic action signatures are not given by the user but rather learned from data. We do so by borrowing ideas from data-driven, two-stream action recognition and model-driven, structured human-object interaction detection. The key idea behind our approach is that we can learn the static and dynamic decomposition of an interaction using a dual-pathway network by leveraging object detections. To do so, we introduce the Motion Guided Attention Fusion mechanism which transfers the motion-centric features learned using object detections to the representation learned from the RGB-based motion pathway. Finally, we conclude with a comprehensive case study on vision based activity detection applied to video surveillance. Using the methods presented in this dissertation, we step towards an intelligent vision system that can detect a particular interaction instance only given a description from a user and depart from requiring massive dataset of labeled training videos. Moreover, as our framework naturally defines a decompositional structure of activities into detectable static/visual attributes, we show that we can simulate necessary training data to acquire attribute detectors when the desired detector is otherwise unavailable. Our approach achieves competitive or superior performance over existing approaches for recognizing fine-grained interactions from realistic videos