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The RadEx workflow based on our task abstraction. The flow is denoted by arrows consists of two phases. Phase One: Discover invalid co‐registrations and segmentation masks. Phase Two (Search and Query): When the data quality is ensured, users are able to search and query the whole cohort. The arrows depict that there is no pre‐defined ordering of the tasks: each task can be executed in any order in the search and query section.
Source publication
Better understanding of the complex processes driving tumor growth and metastases is critical for developing targeted treatment strategies in cancer. Radiomics extracts large amounts of features from medical images which enables radiomic tumor profiling in combination with clinical markers. However, analyzing complex imaging data in combination wit...
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Background
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Citations
... These theories stress self-motivated improvement and the integration of practical, case-based learning in medicine. Guided by these theories, technical solutions, including learning platforms and interactive systems [37,43,68], have been developed to enhance novice physicians' learning experiences. While many platforms use clinical resources like online materials [20], slides, and videos [36,59], interactive systems excel by incorporating diverse multimodal diagnostic data [37,43]. ...
... Guided by these theories, technical solutions, including learning platforms and interactive systems [37,43,68], have been developed to enhance novice physicians' learning experiences. While many platforms use clinical resources like online materials [20], slides, and videos [36,59], interactive systems excel by incorporating diverse multimodal diagnostic data [37,43]. This data encompasses radiology images (e.g., X-rays, CT scans, MRI), clinical texts (e.g., diagnostic reports), and laboratory indicators, significantly improving physicians' retrospective learning [13,68]. ...
Continuous Medical Education (CME) plays a vital role in physicians' ongoing professional development. Beyond immediate diagnoses, physicians utilize multimodal diagnostic data for retrospective learning, engaging in self-directed analysis and collaborative discussions with peers. However, learning from such data effectively poses challenges for novice physicians, including screening and identifying valuable research cases, achieving fine-grained alignment and representation of multimodal data at the semantic level, and conducting comprehensive contextual analysis aided by reference data. To tackle these challenges, we introduce Medillustrator, a visual analytics system crafted to facilitate novice physicians' retrospective learning. Our structured approach enables novice physicians to explore and review research cases at an overview level and analyze specific cases with consistent alignment of multimodal and reference data. Furthermore, physicians can record and review analyzed results to facilitate further retrospection. The efficacy of Medillustrator in enhancing physicians' retrospective learning processes is demonstrated through a comprehensive case study and a controlled in-lab between-subject user study.
... For example, Raidou et al. [55] integrated multiple visualization tech-niques, including timeline, scatterplot, heatmap, brushing and linking, and interactive filters, to explore radiation-induced bladder toxicity in a cohort study using clinical data such as treatment events and patient demographics. Mörth et al. [51] integrated clinical and radiological data, employing heatmaps, boxplots, scatterplots, and decision trees to facilitate the exploration of multiparametric studies for radiomic tumor profiling. Sugathan et al. [68] introduced a longitudinal visualization approach for examining multiple sclerosis lesions over time, enabling both qualitative and quantitative analysis of lesion progression. ...
Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.
Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. The book includes user research, for example, task and requirement analysis, visualization design and algorithmic ideas without going into the details of implementation. This reference will be suitable for researchers and students in visualization and visual analytics in medicine and healthcare, medical image analysis scientists and biomedical engineers in general.
Visualization and visual analytics have become prevalent in public health and clinical medicine, medical flow visualization, multimodal medical visualization and virtual reality in medical education and rehabilitation. Relevant applications now include digital pathology, virtual anatomy and computer-assisted radiation treatment planning.
Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present
DiagnosisAssistant
, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.
Combining elements of biology, chemistry, physics, and medicine, the science of human physiology is complex and multifaceted. In this report, we offer a broad and multiscale perspective on key developments and challenges in visualization for physiology. Our literature search process combined standard methods with a state‐of‐the‐art visual analysis search tool to identify surveys and representative individual approaches for physiology. Our resulting taxonomy sorts literature on two levels. The first level categorizes literature according to organizational complexity and ranges from molecule to organ. A second level identifies any of three high‐level visualization tasks within a given work: exploration, analysis, and communication. The findings of this report may be used by visualization researchers to understand the overarching trends, challenges, and opportunities in visualization for physiology and to provide a foundation for discussion and future research directions in this area.
