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Serial Dependence Study in Medical Image Perception via Generative Models
Serial Dependence Study in Medical Image Perception via Generative Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151428
- ISBN
- 9798384453017
- DDC
- 004
- 저자명
- Ren, Zhihang.
- 서명/저자
- Serial Dependence Study in Medical Image Perception via Generative Models
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 152 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Whitney, David;Yu, Stella X.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약Medical imaging has been critically important for the health and well-being of millions of patients. Although deep learning has been widely studied in the medical imaging area and the performance of deep learning has exceeded human performance in certain medical diagnostic tasks, detecting and diagnosing lesions still depends on the visual system of human observers (radiologists), who completed years of training to scrutinize anomalies. Routinely, radiologists sequentially read batches of medical images one after the other. A basic underlying assumption of radiologists' precise diagnosis is that their perceptions and decisions on a current medical image are completely independent of the previous reading history of medical images. However, recent research proposed that the human visual system has visual serial dependencies at many levels. Visual serial dependence means that what was seen in the past influences (and captures) what is seen and reported at this moment. In this dissertation, we first show that visual serial dependence has a disruptive effect on radiological searches that impairs the accurate detection and recognition of tumors or other structures via naive artificial stimuli. However, the naive artificial stimuli have been noted by both untrained observers and expert radiologists to be less authentic, which can not help to reveal the real scenarios of medical image perception. To solve this issue, we propose and build a generative tool via generative adversarial networks (GANs) to generate authentic medical images, replacing the simple stimuli in future experiments. Using the authentic medical images from the GenAI medical image generation tool, we find that the perception of the current simulated medical image was biased towards the previously seen medical images, which strengthens the evidence of the existence of the visual serial dependence effect in medical image perception. Finally, we collaboratively collect real diagnostic data with a data annotation company. Through meticulous data analysis, we find significant serial dependence effects in perceptual discrimination judgments, which negatively impacted performance measures, including sensitivity, specificity, and error rates. These findings help understand one potential source of systematic bias and errors in medical image perception tasks and hint at useful approaches that could alleviate the errors due to serial dependence.
- 일반주제명
- Computer science
- 일반주제명
- Medical imaging
- 일반주제명
- Bioinformatics
- 키워드
- Deep learning
- 키워드
- Radiology
- 기타저자
- University of California, Berkeley Vision Science
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798384453017
■035 ▼a(MiAaPQ)AAI31294953
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aRen, Zhihang.
■24510▼aSerial Dependence Study in Medical Image Perception via Generative Models
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a152 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Whitney, David;Yu, Stella X.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aMedical imaging has been critically important for the health and well-being of millions of patients. Although deep learning has been widely studied in the medical imaging area and the performance of deep learning has exceeded human performance in certain medical diagnostic tasks, detecting and diagnosing lesions still depends on the visual system of human observers (radiologists), who completed years of training to scrutinize anomalies. Routinely, radiologists sequentially read batches of medical images one after the other. A basic underlying assumption of radiologists' precise diagnosis is that their perceptions and decisions on a current medical image are completely independent of the previous reading history of medical images. However, recent research proposed that the human visual system has visual serial dependencies at many levels. Visual serial dependence means that what was seen in the past influences (and captures) what is seen and reported at this moment. In this dissertation, we first show that visual serial dependence has a disruptive effect on radiological searches that impairs the accurate detection and recognition of tumors or other structures via naive artificial stimuli. However, the naive artificial stimuli have been noted by both untrained observers and expert radiologists to be less authentic, which can not help to reveal the real scenarios of medical image perception. To solve this issue, we propose and build a generative tool via generative adversarial networks (GANs) to generate authentic medical images, replacing the simple stimuli in future experiments. Using the authentic medical images from the GenAI medical image generation tool, we find that the perception of the current simulated medical image was biased towards the previously seen medical images, which strengthens the evidence of the existence of the visual serial dependence effect in medical image perception. Finally, we collaboratively collect real diagnostic data with a data annotation company. Through meticulous data analysis, we find significant serial dependence effects in perceptual discrimination judgments, which negatively impacted performance measures, including sensitivity, specificity, and error rates. These findings help understand one potential source of systematic bias and errors in medical image perception tasks and hint at useful approaches that could alleviate the errors due to serial dependence.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science
■650 4▼aMedical imaging
■650 4▼aBioinformatics
■653 ▼aGenerative models
■653 ▼aDeep learning
■653 ▼aSerial dependence
■653 ▼aGenerative adversarial networks
■653 ▼aRadiology
■690 ▼a0984
■690 ▼a0574
■690 ▼a0715
■690 ▼a0800
■71020▼aUniversity of California, Berkeley▼bVision Science.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161668▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
