LUCAS: LUng CAncer Screening with Multimodal Biomarkers

Daza, Laura; Castillo, Ángela; Escobar, María; Valencia, Sergio; Pinzón, Bibiana

doi:10.1007/978-3-030-60946-7_12

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Details on the framework can be found on https://github.com/wiesenfa/challengeR . We have observed that the toolkit has already been used by several users for challenge evaluation 28 , 29 and algorithm validation 30 in general. Other authors have adopted concepts from the toolkit, such as bootstrapping for investigating ranking variability 31 .…”

Section: Open-source Challenge Visualization Toolkitmentioning

confidence: 99%

Methods and open-source toolkit for analyzing and visualizing challenge results

Wiesenfarth

Reinke

Landman

et al. 2021

Sci Rep

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Grand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.

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Section: Open-source Challenge Visualization Toolkitmentioning

confidence: 99%

Methods and open-source toolkit for analyzing and visualizing challenge results

Wiesenfarth

Reinke

Landman

et al. 2021

Sci Rep

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“…Then, the vectors from each modality are sent individually through two attention-based blocks, then concatenated into a joint feature space to predict a possible cardiovascular disease and generate a free-text "impression" of the condition. Other joint representation models follow simpler methods, simply extracting baseline features from a pretrained model and concatenating them Daza et al (2020); .…”

Section: Tablementioning

confidence: 99%

“…Brain-based ML studies are also popular because of the wide availability of brain images and a strong interest in applying ML models in clinical neuroradiology. However, recent models encompass a myriad of other clinical scenarios predicting lung cancer presence (Daza et al, 2020), segmenting soft tissue sarcomas (Neubauer et al, 2020), classifying breast lesions (Habib et al, 2020), and predicting therapy response , among others, by amalgamating and cross-referencing modalities such as CT images (Daza et al, 2020;Neubauer et al, 2020), blood tests , electronic health record (EHR) data Daza et al, 2020), mammography images (Habib et al, 2020), and ultrasound (Habib et al, 2020).…”

Section: Fusionmentioning

confidence: 99%

Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects

Warner,

Lee,

Hsu

et al. 2024

Int J Comput Vis

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Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of “big data” in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.

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“…We use LUCAS (LUng CAncer Simple set) for this, which contains simulated data created by causal Bayesian networks with binary variables. Daza et al (2020) mentions that these examples are entirely made up and are mainly used for illustrating purposes.…”

Section: Ground Truth Comparison: the Lucas Datasetmentioning

confidence: 99%

Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

Papamichalis¹,

Ray²,

Bilionis³

et al. 2021

Preprint

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Probabilistic machine learning models are often insufficient to help with decisions on interventions because those models find correlations -not causal relationships. If observational data is only available and experimentations are infeasible, the correct approach to study the impact of an intervention is to invoke Pearl's causality framework. Even that framework assumes that the underlying causal graph is known, which is seldom the case in practice. When the causal structure is not known, one may use out-of-the-box algorithms to find causal dependencies from observational data. However, there exists no method that also accounts for the decision-maker's prior knowledge when developing the causal structure either. The objective of this paper is to develop rational approaches for making decisions from observational data in the presence of causal graph uncertainty and prior knowledge from the decision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to infer set of causal graphs that can represent the data generation process. We provide decisions by computing the expected value and risk of potential interventions explicitly. We demonstrate our approach by applying them in different example contexts.

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LUCAS: LUng CAncer Screening with Multimodal Biomarkers

Cited by 7 publications

References 15 publications

Methods and open-source toolkit for analyzing and visualizing challenge results

Methods and open-source toolkit for analyzing and visualizing challenge results

Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects

Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

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