@article{grossmann-2022-conceptSplatters, title = "Concept splatters: Exploration of latent spaces based on human interpretable concepts", author = "Nicolas Grossmann and Eduard Gr\"{o}ller and Manuela Waldner", year = "2022", abstract = "Similarity maps show dimensionality-reduced activation vectors of a high number of data points and thereby can help to understand which features a neural network has learned from the data. However, similarity maps have severely limited expressiveness for large datasets with hundreds of thousands of data instances and thousands of labels, such as ImageNet or word2vec. In this work, we present “concept splatters” as a scalable method to interactively explore similarities between data instances as learned by the machine through the lens of human-understandable semantics. Our approach enables interactive exploration of large latent spaces on multiple levels of abstraction. We present a web-based implementation that supports interactive exploration of tens of thousands of word vectors of word2vec and CNN feature vectors of ImageNet. In a qualitative study, users could effectively discover spurious learning strategies of the network, ambiguous labels, and could characterize reasons for potential confusion.", month = apr, doi = "10.1016/j.cag.2022.04.013", issn = "1873-7684", journal = "Computers and Graphics", pages = "12", volume = "105", publisher = "Elsevier", pages = "73--84", keywords = "Concept spaces, Latent spaces, Similarity maps, Visual exploratory analysis", URL = "https://www.cg.tuwien.ac.at/research/publications/2022/grossmann-2022-conceptSplatters/", } @inproceedings{grossmann-2021-layout, title = "Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation", author = "Nicolas Grossmann and J\"{u}rgen Bernard and Michael Sedlmair and Manuela Waldner", year = "2021", abstract = "In visual interactive labeling, users iteratively assign labels to data items until the machine model reaches an acceptable accuracy. A crucial step of this process is to inspect the model's accuracy and decide whether it is necessary to label additional elements. In scenarios with no or very little labeled data, visual inspection of the predictions is required. Similarity-preserving scatterplots created through a dimensionality reduction algorithm are a common visualization that is used in these cases. Previous studies investigated the effects of layout and image complexity on tasks like labeling. However, model evaluation has not been studied systematically. We present the results of an experiment studying the influence of image complexity and visual grouping of images on model accuracy estimation. We found that users outperform traditional automated approaches when estimating a model's accuracy. Furthermore, while the complexity of images impacts the overall performance, the layout of the items in the plot has little to no effect on estimations.", month = oct, publisher = "IEEE Computer Society Press", event = "IEEE Visualization Conference (VIS)", doi = "10.1109/VIS49827.2021.9623326", booktitle = "IEEE Visualization Conference (VIS)", pages = "5", pages = "61--65", URL = "https://www.cg.tuwien.ac.at/research/publications/2021/grossmann-2021-layout/", } @article{wu-2021-vi, title = "Visualization working group at TU Wien: Visibile Facimus Quod Ceteri Non Possunt", author = "Hsiang-Yun Wu and Aleksandr Amirkhanov and Nicolas Grossmann and Tobias Klein and David Kou\v{r}il and Haichao Miao and Laura R. Luidolt and Peter Mindek and Renata Raidou and Ivan Viola and Manuela Waldner and Eduard Gr\"{o}ller", year = "2021", abstract = "Building-up and running a university-based research group is a multi-faceted undertaking. The visualization working group at TU Wien (vis-group) has been internationally active over more than 25 years. The group has been acting in a competitive scientific setting where sometimes contradicting multiple objectives require trade-offs and optimizations. Research-wise the group has been performing basic and applied research in visualization and visual computing. Teaching-wise the group has been involved in undergraduate and graduate lecturing in (medical) visualization and computer graphics. To be scientifically competitive requires to constantly expose the group and its members to a strong international competition at the highest level. This necessitates to shield the members against the ensuing pressures and demands and provide (emotional) support and encouragement. Internally, the vis-group has developed a unique professional and social interaction culture: work and celebrate, hard and together. This has crystallized into a nested, recursive, and triangular organization model, which concretizes what it takes to make a research group successful. The key elements are the creative and competent vis-group members who collaboratively strive for (scientific) excellence in a socially enjoyable environment.", month = mar, doi = "https://doi.org/10.1016/j.visinf.2021.02.003", journal = "Visual Informatics", volume = "5", pages = "76--84", URL = "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/", } @article{furmanova_2020, title = "VAPOR: Visual Analytics for the Exploration of Pelvic Organ Variability in Radiotherapy", author = "Katar\'{i}na Furmanov\'{a} and Nicolas Grossmann and Ludvig Paul Muren and Oscar Casares-Magaz and Vitali Moiseenko and John P. Einck and Eduard Gr\"{o}ller and Renata Raidou", year = "2020", abstract = "In radiation therapy (RT) for prostate cancer, changes in patient anatomy during treatment might lead to inadequate tumor coverage and higher irradiation of healthy tissues in the nearby pelvic organs. Exploring and analyzing anatomical variability throughout the course of RT can support the design of more robust treatment strategies, while identifying patients that are prone to radiation-induced toxicity. We present VAPOR, a novel application for the exploration of pelvic organ variability in a cohort of patients, across the entire treatment process. Our application addresses: (i) the global exploration and analysis of anatomical variability in an abstracted tabular view, (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated, and (iii) the correlation of anatomical variability with radiation doses and potential toxicity. The workflow is based on available retrospective cohort data, which include segmentations of the bladder, the prostate, and the rectum through the entire treatment period. VAPOR is applied to four usage scenarios, which were conducted with two medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment adaptation to anatomical changes.", month = oct, doi = "https://doi.org/10.1016/j.cag.2020.07.001", journal = "Computer & Graphics", note = "Special Section on VCBM 2019", volume = "91", pages = "25--38", URL = "https://www.cg.tuwien.ac.at/research/publications/2020/furmanova_2020/", } @inproceedings{Iijima-2020-iV, title = "Visualization of Semantic Differential Studies with a Large Number of Images, Participants and Attributes", author = "Akari Iijima and Takayuki Itoh and Hsiang-Yun Wu and Nicolas Grossmann", year = "2020", abstract = "The Semantic Differential (SD) Method is a rating scale to measure the semantics. Attributes of SD are constructed by collecting the responses of participant’s impres- sions of the objects expressed through Likert scales representing multiple contrasting with some adjective pairs, for example, dark and bright, formal and casual, etc. Impression evaluation can be used as an index that reflects a human subjective feelings to some extent. Impression evaluations using the SD method consist of the responses of many participants, and therefore, the individual differences in the impressions of the participants greatly affect the content of the data. In this study, we propose a visualization system to analyze three aspects of SD, objects (images), participants, and attributes defined by adjective pairs. We visualize the impression evaluation data by applying dimension reduction so that, users can discover the trends and outliers of the data, such as images that are hard to judge or participants that act unpredictably. The system firstly visualizes the attributes or color distribution of the images by applying a dimensional reduction method to the impression or RGB values of each image. Then, our approach displays the average and median of each attribute near the images. This way, we can visualize the three aspects of objects, participants and attributes on a single screen and observe the relationships between image features and user impressions / attribute space. We introduce visualization examples of our system with the dataset inviting 21 participants who performed impression evaluations with 300 clothing images.", month = sep, event = "The 24th International Conference on Information Visualisation (iV2020)", booktitle = "Proceedings of the 24th International Conference on Information Visualisation (iV2020)", pages = "1--6", URL = "https://www.cg.tuwien.ac.at/research/publications/2020/Iijima-2020-iV/", } @inproceedings{Kuroko-2020-iV, title = "Visualization of Correlations between Places of Music Listening and Acoustic Features ", author = "Narumi Kuroko and Hayato Ohya and Takayuki Itoh and Nicolas Grossmann and Hsiang-Yun Wu", year = "2020", abstract = "Users often choose songs with respect to special situations and environments. We designed and developed a music recommendation method inspired by this fact. This method selects songs based on the distribution of acoustic features of the songs listened by a user at particular places that have higher ordinariness for the user. It is important to verify the relationship between the places where the songs are listened to and the acoustic features in this. Hence, we conducted the visualization to explore potential correlations between geographic locations and the music features of single users. In this paper, we designed an interactive visualization tool methods and results for the analysis of the relationship between the places and the acoustic features while listening to the songs.", month = sep, event = "The 24th International Conference on Information Visualisation (iV2020)", booktitle = "Proceedings of the 24th International Conference on Information Visualisation (iV2020)", pages = "1--6", URL = "https://www.cg.tuwien.ac.at/research/publications/2020/Kuroko-2020-iV/", } @inproceedings{raidou_visgap2020, title = "Lessons Learnt from Developing Visual Analytics Applications for Adaptive Prostate Cancer Radiotherapy", author = "Renata Raidou and Katar\'{i}na Furmanov\'{a} and Nicolas Grossmann and Oscar Casares-Magaz and Vitali Moiseenko and John P. Einck and Eduard Gr\"{o}ller and Ludvig Paul Muren", year = "2020", abstract = "In radiotherapy (RT), changes in patient anatomy throughout the treatment period might lead to deviations between planned and delivered dose, resulting in inadequate tumor coverage and/or overradiation of healthy tissues. Adapting the treatment to account for anatomical changes is anticipated to enable higher precision and less toxicity to healthy tissues. Corresponding tools for the in-depth exploration and analysis of available clinical cohort data were not available before our work. In this paper, we discuss our on-going process of introducing visual analytics to the domain of adaptive RT for prostate cancer. This has been done through the design of three visual analytics applications, built for clinical researchers working on the deployment of robust RT treatment strategies. We focus on describing our iterative design process, and we discuss the lessons learnt from our fruitful collaboration with clinical domain experts and industry, interested in integrating our prototypes into their workflow.", month = may, event = "EGEV2020 - VisGap Workshop", booktitle = "The Gap between Visualization Research and Visualization Software (VisGap) (2020)", pages = "1--8", keywords = "Visual Analytics, Life and Medical Sciences", URL = "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_visgap2020/", } @misc{grossmann_2019_pelvisrunner_poster, title = "Pelvis Runner: A Visual Analytics Tool for Pelvic Organ Variability Exploration in Prostate Cancer Cohorts", author = "Nicolas Grossmann and Oscar Casares-Magaz and Ludvig Paul Muren and Vitali Moiseenko and John P. Einck and Eduard Gr\"{o}ller and Renata Raidou", year = "2019", abstract = "Pelvis Runner is a visual analysis tool for the exploration of the variability of segmented pelvic organs in multiple patients, across the course of radiation therapy treatment. Radiation treatment is performed through the course of weeks, during which the anatomy of the patient changes. This variability may be responsible for side effects, due to the potential over-irradiation of healthy tissues. Exploring and analyzing organ variability in patient cohorts can help clinical researchers to design more robust treatment strategies. Our work addresses, first, the global exploration and analysis of pelvic organ shape variability in an abstracted tabular view for the entire cohort. Second, local exploration and analysis of the variability are provided on-demand in anatomical 2D/3D views for cohort partitions. The Pelvis Runner has been evaluated by two clinical researchers and is a promising basis for the exploration of pelvic organ variability.", month = oct, event = "IEEE VIS VAST", Conference date = "Poster presented at IEEE VIS VAST (2019-10-20--2019-10-25)", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/grossmann_2019_pelvisrunner_poster/", } @inproceedings{raidou_2019_pelvisrunner, title = "Pelvis Runner: Visualizing Pelvic Organ Variability in a Cohort of Radiotherapy Patients", author = "Nicolas Grossmann and Oscar Casares-Magaz and Ludvig Paul Muren and Vitali Moiseenko and John P. Einck and Eduard Gr\"{o}ller and Renata Raidou", year = "2019", abstract = "In radiation therapy, anatomical changes in the patient might lead to deviations between the planned and delivered dose--including inadequate tumor coverage, and overradiation of healthy tissues. Exploring and analyzing anatomical changes throughout the entire treatment period can help clinical researchers to design appropriate treatment strategies, while identifying patients that are more prone to radiation-induced toxicity. We present the Pelvis Runner, a novel application for exploring the variability of segmented pelvic organs in multiple patients, across the entire radiation therapy treatment process. Our application addresses (i) the global exploration and analysis of pelvic organ shape variability in an abstracted tabular view and (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated. The workflow is based on available retrospective cohort data, which incorporate segmentations of the bladder, the prostate, and the rectum through the entire radiation therapy process. The Pelvis Runner is applied to four usage scenarios, which were conducted with two clinical researchers, i.e., medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment plan adaptation to anatomical changes.", month = sep, event = "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", doi = "10.2312/vcbm.20191233", booktitle = "Eurographics Workshop on Visual Computing for Biology and Medicine (2019)", pages = "69--78", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/raidou_2019_pelvisrunner/", } @mastersthesis{Grossmann_MA, title = "Pelvis Runner - Comparative Visualization of Anatomical Changes", author = "Nicolas Grossmann", year = "2019", abstract = "Pelvic organs such as the bladder, rectum or prostate have highly variable shapes that change over time, due to their soft and flexible tissue and varying filling. Recent clinical work suggests that these variations might affect the effectiveness of radiation therapy treatment in patients with prostate cancer. Although in clinical practice small correction steps are performed to re-align the treated region if the organs are shifted, a more in-depth understanding and modeling might prove beneficial for the adaptation of the employed treatment planning strategy. To evaluate the viability and to account for the variability in the population of certain treatment strategies, cohort studies are performed analyzing the shape and position variability of pelvic organs. In this thesis, we propose a web-based tool that is able to analyze a cohort of pelvic organs from 24 patients across 13 treatment instances. Hereby we have two goals: On the one hand, we want to support medical researchers analyzing large groups of patients for their shape variability and the possible correlations to side effects. On the other hand, we want to provide support for medical experts performing individual patient treatment planning. Our tool offers both the option to analyze a large cohort of different organ shapes, by first modeling them in a shape space and then analyzing the shape variations on a per-patient basis. While this first part aims at providing users with an overview of the data, we also give them the option to perform a detailed shape analysis, where we highlight the statistically aggregated shape of a patient or a specified group using a contour variability plot. Finally, we demonstrate several possible usage scenarios for our tool and perform an informal evaluation with two medical experts. Our tool is the first significant step in supporting medical experts in demonstrating the need for adaptation in radiation therapy treatments to account for shape variability.", month = aug, address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Research Unit of Computer Graphics, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien", URL = "https://www.cg.tuwien.ac.at/research/publications/2019/Grossmann_MA/", } @article{raidou2018visualflatter, title = "VisualFlatter - Visual Analysis of Distortions in the Projection of Biomedical Structures", author = "Nicolas Grossmann and Thomas K\"{o}ppel and Eduard Gr\"{o}ller and Renata Raidou", year = "2018", abstract = "Projections of complex anatomical or biological structures from 3D to 2D are often used by visualization and domain experts to facilitate inspection and understanding. Representing complex structures, such as organs or molecules, in a simpler 2D way often requires less interaction, while enabling comparability. However, the most commonly employed projection methods introduce size or shape distortions, in the resulting 2D representations. While simple projections display known distortion patterns, more complex projection algorithms are not easily predictable.We propose the VisualFlatter, a visual analysis tool that enables visualization and domain experts to explore and analyze projection-induced distortions, in a structured way. Our tool provides a way to identify projected regions with semantically relevant distortions and allows users to comparatively analyze distortion outcomes, either from alternative projection methods or due to different setups through the projection pipeline. The user is given the ability to improve the initial projection configuration, after comparing different setups. We demonstrate the functionality of our tool using four scenarios of 3D to 2D projections, conducted with the help of domain or visualization experts working on different application fields. We also performed a wider evaluation with 13 participants, familiar with projections, to assess the usability and functionality of the Visual Flatter.", month = sep, journal = "Eurographics Proceedings", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/raidou2018visualflatter/", } @bachelorsthesis{grossmann-2016-baa, title = "Extracting Sensor Specific Noise Models", author = "Nicolas Grossmann", year = "2017", abstract = "With the growing number of consumer-oriented depth sensors like the Kinect or the newly released Phab2Pro, the question of how precise these sensors are arises. In this thesis we want to evaluate the average noise in the generated depth measurements in both the axial direction and the lateral directions. As part of a two-part project this thesis will view the noise’s development with varying distance and angle. Finally, we will present and evaluate two models describing the noise behavior, with the first being derived from solely this thesis’ measurements and the second one being a combination of the previous model and a model of a colleague. This derived models can be used in a post-processing step to filter the generated depth images.", month = aug, note = "1", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", keywords = "noise model, surface reconstruction, sensor noise", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/grossmann-2016-baa/", } @runphdthesis{grossmann-thesis, title = "(unknown)", author = "Nicolas Grossmann", URL = "https://www.cg.tuwien.ac.at/research/publications/ongoing/grossmann-thesis/", } @article{wu-2021, title = "Visualization working group at TU Wien: Visible Facimus Quod Ceteri Non Possunt", author = "Hsiang-Yun Wu and Aleksandr Amirkhanov and Nicolas Grossmann and Tobias Klein and David Kou\v{r}il and Haichao Miao and Laura R. Luidolt and Peter Mindek and Renata Raidou and Ivan Viola and Manuela Waldner and Eduard Gr\"{o}ller", abstract = "Building-up and running a university-based research group is a multi-faceted undertaking. The visualization working group at TU Wien (vis-group) has been internationally active over more than 25 years. The group has been acting in a competitive scientific setting where sometimes contradicting multiple objectives require trade-offs and optimizations. Research-wise the group has been performing basic and applied research in visualization and visual computing. Teaching-wise the group has been involved in undergraduate and graduate lecturing in (medical) visualization and computer graphics. To be scientifically competitive requires to constantly expose the group and its members to a strong international competition at the highest level. This necessitates to shield the members against the ensuing pressures and demands and provide (emotional) support and encouragement. Internally, the vis-group has developed a unique professional and social interaction culture: work and celebrate, hard and together. This has crystallized into a nested, recursive, and triangular organization model, which concretizes what it takes to make a research group successful. The key elements are the creative and competent vis-group members who collaboratively strive for (scientific) excellence in a socially enjoyable environment.", doi = "https://doi.org/10.1016/j.visinf.2021.02.003", journal = "Visual Informatics", volume = "5", pages = "76--84", URL = "https://www.cg.tuwien.ac.at/research/publications/ongoing/wu-2021/", }