Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

차량용 헤드업 디스플레이 설계에 관한 인간공학 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 박우진.Head-up display (HUD) systems were introduced into the automobile industry as a means for improving driving safety. They superimpose safety-critical information on top of the drivers forward field of view and thereby help drivers keep their eyes forward while driving. Since the first introduction about three decades ago, automotive HUDs have been available in various commercial vehicles. Despite the long history and potential benefits of automotive HUDs, however, the design of useful automotive HUDs remains a challenging problem. In an effort to contribute to the design of useful automotive HUDs, this doctoral dissertation research conducted four studies. In Study 1, the functional requirements of automotive HUDs were investigated by reviewing the major automakers' automotive HUD products, academic research studies that proposed various automotive HUD functions, and previous research studies that surveyed drivers HUD information needs. The review results indicated that: 1) the existing commercial HUDs perform largely the same functions as the conventional in-vehicle displays, 2) past research studies proposed various HUD functions for improving driver situation awareness and driving safety, 3) autonomous driving and other new technologies are giving rise to new HUD information, and 4) little research is currently available on HUD users perceived information needs. Based on the review results, this study provides insights into the functional requirements of automotive HUDs and also suggests some future research directions for automotive HUD design. In Study 2, the interface design of automotive HUDs for communicating safety-related information was examined by reviewing the existing commercial HUDs and display concepts proposed by academic research studies. Each display was analyzed in terms of its functions, behaviors and structure. Also, related human factors display design principles, and, empirical findings on the effects of interface design decisions were reviewed when information was available. The results indicated that: 1) information characteristics suitable for the contact-analog and unregistered display formats, respectively, are still largely unknown, 2) new types of displays could be developed by combining or mixing existing displays or display elements at both the information and interface element levels, and 3) the human factors display principles need to be used properly according to the situation and only to the extent that the resulting display respects the limitations of the human information processing, and achieving balance among the principles is important to an effective design. On the basis of the review results, this review suggests design possibilities and future research directions on the interface design of safety-related automotive HUD systems. In Study 3, automotive HUD-based take-over request (TOR) displays were developed and evaluated in terms of drivers take-over performance and visual scanning behavior in a highly automated driving situation. Four different types of TOR displays were comparatively evaluated through a driving simulator study - they were: Baseline (an auditory beeping alert), Mini-map, Arrow, and Mini-map-and-Arrow. Baseline simply alerts an imminent take-over, and was always included when the other three displays were provided. Mini-map provides situational information. Arrow presents the action direction information for the take-over. Mini-map-and-Arrow provides the action direction together with the relevant situational information. This study also investigated the relationship between drivers initial trust in the TOR displays and take-over and visual scanning behavior. The results indicated that providing a combination of machine-made decision and situational information, such as Mini-map-and-Arrow, yielded the best results overall in the take-over scenario. Also, drivers initial trust in the TOR displays was found to have significant associations with the take-over and visual behavior of drivers. The higher trust group primarily relied on the proposed TOR displays, while the lower trust group tended to more check the situational information through the traditional displays, such as side-view or rear-view mirrors. In Study 4, the effect of interactive HUD imagery location on driving and secondary task performance, driver distraction, preference, and workload associated with use of scrolling list while driving were investigated. A total of nine HUD imagery locations of full-windshield were examined through a driving simulator study. The results indicated the HUD imagery location affected all the dependent measures, that is, driving and task performance, drivers visual distraction, preference and workload. Considering both objective and subjective evaluations, interactive HUDs should be placed near the driver's line of sight, especially near the left-bottom on the windshield.자동차 헤드업 디스플레이는 차내 디스플레이 중 하나로 운전자에게 필요한 정보를 전방에 표시함으로써, 운전자가 운전을 하는 동안 전방으로 시선을 유지할 수 있게 도와준다. 이를 통해 운전자의 주의 분산을 줄이고, 안전을 향상시키는데 도움이 될 수 있다. 자동차 헤드업 디스플레이 시스템은 약 30년 전 운전자의 안전을 향상시키기 위한 수단으로 자동차 산업에 처음 도입된 이래로 현재까지 다양한 상용차에서 사용되고 있다. 안전과 편의 측면에서 자동차 헤드업 디스플레이의 사용은 점점 더 증가할 것으로 예상된다. 그러나 이러한 자동차 헤드업 디스플레이의 잠재적 이점과 발전 가능성에도 불구하고, 유용한 자동차 헤드업 디스플레이를 설계하는 것은 여전히 어려운 문제이다. 이에 본 연구는 이러한 문제를 해결하고, 궁극적으로 유용한 자동차 헤드업 디스플레이 설계에 기여하고자 총 4가지 연구를 수행하였다. 첫 번째 연구는 자동차 헤드업 디스플레이의 기능 요구 사항과 관련된 것으로서, 헤드업 디스플레이 시스템을 통해 어떤 정보를 제공할 것인가에 대한 답을 구하고자 하였다. 이에 주요 자동차 제조업체들의 헤드업 디스플레이 제품들과, 자동차 헤드업 디스플레이의 다양한 기능들을 제안한 학술 연구, 그리고 운전자의 정보 요구 사항들을 체계적 문헌 고찰 방법론을 통해 포괄적으로 조사하였다. 자동차 헤드업 디스플레이의 기능적 요구 사항에 대하여 개발자, 연구자, 사용자 측면을 모두 고려한 통합된 지식을 전달하고, 이를 통해 자동차 헤드업 디스플레이의 기능 요구 사항에 대한 향후 연구 방향을 제시하였다. 두 번째 연구는 안전 관련 정보를 제공하는 자동차 헤드업 디스플레이의 인터페이스 설계와 관련된 것으로, 헤드업 디스플레이 시스템을 통해 안전 관련 정보를 어떻게 제공할 것인가에 대한 답을 구하고자 하였다. 실제 자동차들의 헤드업 디스플레이 시스템에서는 어떤 디스플레이 컨셉들이 사용되었는지, 그리고 학계에서 제안된 디스플레이 컨셉들에는 어떤 것들이 있는지 체계적 문헌 고찰 방법론을 통해 검토하였다. 검토된 결과는 각 디스플레이의 기능과 구조, 그리고 작동 방식에 따라 정리되었고, 관련된 인간공학적 디스플레이 설계 원칙과 실험적 연구 결과들을 함께 검토하였다. 검토된 결과를 바탕으로 안전 관련 정보를 제공하는 자동차 헤드업 디스플레이의 인터페이스 설계에 대한 향후 연구 방향을 제시하였다. 세 번째 연구는 자동차 헤드업 디스플레이 기반의 제어권 전환 관련 인터페이스 설계와 평가에 관한 것이다. 제어권 전환이란, 자율주행 상태에서 운전자가 직접 운전을 하는 수동 운전 상태로 전환이 되는 것을 의미한다. 따라서 갑작스런 제어권 전환 요청이 발생하는 경우, 운전자가 안전하게 대처하기 위해서는 빠른 상황 파악과 의사 결정이 필요하게 되고, 이를 효과적으로 도와주기 위한 인터페이스 설계에 대해 연구할 필요성이 있다. 이에 본 연구에서는 자동차 헤드업 디스플레이 기반의 총 4개의 제어권 전환 관련 디스플레이(기준 디스플레이, 미니맵 디스플레이, 화살표 디스플레이, 미니맵과 화살표 디스플레이)를 제안하였고, 제안된 디스플레이 대안들은 주행 시뮬레이터 실험을 통해 제어권 전환 수행 능력과 안구의 움직임 패턴, 그리고 사용자의 주관적 평가 측면에서 평가되었다. 또한 제안된 디스플레이 대안들에 대해 운전자들의 초기 신뢰도 값을 측정하여 각 디스플레이에 따른 운전자들의 평균 신뢰도 점수에 따라 제어권 전환 수행 능력과 안구의 움직임 패턴, 그리고 주관적 평가가 어떻게 달라지는지 분석하였다. 실험 결과, 제어권 전환 상황에서 자동화된 시스템이 제안하는 정보와 그와 관련된 주변 상황 정보를 함께 제시해 주는 디스플레이가 가장 좋은 결과를 보여주었다. 또한 각 디스플레이에 대한 운전자의 초기 신뢰도 점수는 디스플레이의 실제 사용 행태와 밀접한 관련이 있음을 알 수 있었다. 신뢰도 점수에 따라 신뢰도가 높은 그룹과 낮은 그룹으로 분류되었고, 신뢰도가 높은 그룹은 제안된 디스플레이들이 보여주는 정보를 주로 믿고 따르는 경향이 있었던 반면, 신뢰도가 낮은 그룹은 룸 미러나 사이드 미러를 통해 주변 상황 정보를 더 확인 하는 경향을 보였다. 네 번째 연구는 전면 유리창에서의 인터랙티브 헤드업 디스플레이의 최적 위치를 결정하는 것으로서 주행 시뮬레이터 실험을 통해 디스플레이의 위치에 따라 운전자의 주행 수행 능력, 인터랙티브 디스플레이 조작 관련 과업 수행 능력, 시각적 주의 분산, 선호도, 그리고 작업 부하가 평가되었다. 헤드업 디스플레이의 위치는 전면 유리창에서 일정한 간격으로 총 9개의 위치가 고려되었다. 본 연구에서 활용된 인터랙티브 디스플레이는 음악 선택을 위한 스크롤 방식의 단일 디스플레이였고, 운전대에 장착된 버튼을 통해 디스플레이를 조작하였다. 실험 결과, 인터랙티브 헤드업 디스플레이의 위치가 모든 평가 척도, 즉 주행 수행 능력, 디스플레이 조작 과업 수행 능력, 시각적 주의 분산, 선호도, 그리고 작업 부하에 영향을 미침을 알 수 있었다. 모든 평가 지표를 고려했을 때, 인터랙티브 헤드업 디스플레이의 위치는 운전자가 똑바로 전방을 바라볼 때의 시야 구간, 즉 전면 유리창에서의 왼쪽 아래 부근이 가장 최적인 것으로 나타났다.Abstract i Contents v List of Tables ix List of Figures x Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Objectives and Questions 8 1.3 Structure of the Thesis 11 Chapter 2 Functional Requirements of Automotive Head-Up Displays: A Systematic Review of Literature from 1994 to Present 13 2.1 Introduction 13 2.2 Method 15 2.3 Results 17 2.3.1 Information Types Displayed by Existing Commercial Automotive HUD Systems 17 2.3.2 Information Types Previously Suggested for Automotive HUDs by Research Studies 28 2.3.3 Information Types Required by Drivers (users) for Automotive HUDs and Their Relative Importance 35 2.4 Discussion 39 2.4.1 Information Types Displayed by Existing Commercial Automotive HUD Systems 39 2.4.2 Information Types Previously Suggested for Automotive HUDs by Research Studies 44 2.4.3 Information Types Required by Drivers (users) for Automotive HUDs and Their Relative Importance 48 Chapter 3 A Literature Review on Interface Design of Automotive Head-Up Displays for Communicating Safety-Related Information 50 3.1 Introduction 50 3.2 Method 52 3.3 Results 55 3.3.1 Commercial Automotive HUDs Presenting Safety-Related Information 55 3.3.2 Safety-Related HUDs Proposed by Academic Research 58 3.4 Discussion 74 Chapter 4 Development and Evaluation of Automotive Head-Up Displays for Take-Over Requests (TORs) in Highly Automated Vehicles 78 4.1 Introduction 78 4.2 Method 82 4.2.1 Participants 82 4.2.2 Apparatus 82 4.2.3 Automotive HUD-based TOR Displays 83 4.2.4 Driving Scenario 86 4.2.5 Experimental Design and Procedure 87 4.2.6 Experiment Variables 88 4.2.7 Statistical Analyses 91 4.3 Results 93 4.3.1 Comparison of the Proposed TOR Displays 93 4.3.2 Characteristics of Drivers Initial Trust in the four TOR Displays 102 4.3.3 Relationship between Drivers Initial Trust and Take-over and Visual Behavior 104 4.4 Discussion 113 4.4.1 Comparison of the Proposed TOR Displays 113 4.4.2 Characteristics of Drivers Initial Trust in the four TOR Displays 116 4.4.3 Relationship between Drivers Initial Trust and Take-over and Visual Behavior 117 4.5 Conclusion 119 Chapter 5 Human Factors Evaluation of Display Locations of an Interactive Scrolling List in a Full-windshield Automotive Head-Up Display System 121 5.1 Introduction 121 5.2 Method 122 5.2.1 Participants 122 5.2.2 Apparatus 123 5.2.3 Experimental Tasks and Driving Scenario 123 5.2.4 Experiment Variables 124 5.2.5 Experimental Design and Procedure 126 5.2.6 Statistical Analyses 126 5.3 Results 127 5.4 Discussion 133 5.5 Conclusion 135 Chapter 6 Conclusion 137 6.1 Summary and Implications 137 6.2 Future Research Directions 139 Bibliography 143 Apeendix A. Display Layouts of Some Commercial HUD Systems Appendix B. Safety-related Displays Provided by the Existing Commercial HUD Systems Appendix C. Safety-related HUD displays Proposed by Academic Research 국문초록 187Docto

Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360 Image Outpainting

360 images, with a field-of-view (FoV) of 180x360, provide immersive and realistic environments for emerging virtual reality (VR) applications, such as virtual tourism, where users desire to create diverse panoramic scenes from a narrow FoV photo they take from a viewpoint via portable devices. It thus brings us to a technical challenge: `How to allow the users to freely create diverse and immersive virtual scenes from a narrow FoV image with a specified viewport?' To this end, we propose a transformer-based 360 image outpainting framework called Dream360, which can generate diverse, high-fidelity, and high-resolution panoramas from user-selected viewports, considering the spherical properties of 360 images. Compared with existing methods, e.g., [3], which primarily focus on inputs with rectangular masks and central locations while overlooking the spherical property of 360 images, our Dream360 offers higher outpainting flexibility and fidelity based on the spherical representation. Dream360 comprises two key learning stages: (I) codebook-based panorama outpainting via Spherical-VQGAN (S-VQGAN), and (II) frequency-aware refinement with a novel frequency-aware consistency loss. Specifically, S-VQGAN learns a sphere-specific codebook from spherical harmonic (SH) values, providing a better representation of spherical data distribution for scene modeling. The frequency-aware refinement matches the resolution and further improves the semantic consistency and visual fidelity of the generated results. Our Dream360 achieves significantly lower Frechet Inception Distance (FID) scores and better visual fidelity than existing methods. We also conducted a user study involving 15 participants to interactively evaluate the quality of the generated results in VR, demonstrating the flexibility and superiority of our Dream360 framework.Comment: 11 pages, accepted to IEEE VR 202