Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Marcinkevics, Ricards"'
Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correc
Externí odkaz:
http://arxiv.org/abs/2406.19272
Recently, interpretable machine learning has re-explored concept bottleneck models (CBM). An advantage of this model class is the user's ability to intervene on predicted concept values, affecting the downstream output. In this work, we introduce a m
Externí odkaz:
http://arxiv.org/abs/2401.13544
Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of the underlying decision rule. As a result, practitioners are often unawa
Externí odkaz:
http://arxiv.org/abs/2305.19671
Autor:
Marcinkevičs, Ričards, Wolfertstetter, Patricia Reis, Klimiene, Ugne, Chin-Cheong, Kieran, Paschke, Alyssia, Zerres, Julia, Denzinger, Markus, Niederberger, David, Wellmann, Sven, Ozkan, Ece, Knorr, Christian, Vogt, Julia E.
Publikováno v:
Medical Image Analysis, 91, 103042 (2024)
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, d
Externí odkaz:
http://arxiv.org/abs/2302.14460
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `
Externí odkaz:
http://arxiv.org/abs/2212.12303
Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2208.00781
Autor:
Manduchi, Laura, Marcinkevičs, Ričards, Massi, Michela C., Weikert, Thomas, Sauter, Alexander, Gotta, Verena, Müller, Timothy, Vasella, Flavio, Neidert, Marian C., Pfister, Marc, Stieltjes, Bram, Vogt, Julia E.
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient
Externí odkaz:
http://arxiv.org/abs/2106.05763
Autor:
Marcinkevičs, Ričards, Vogt, Julia E.
Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2101.07600
Autor:
Marcinkevičs, Ričards, Vogt, Julia E.
In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natu
Externí odkaz:
http://arxiv.org/abs/2012.01805
Autor:
Marcinkevičs, Ričards, Reis Wolfertstetter, Patricia, Klimiene, Ugne, Chin-Cheong, Kieran, Paschke, Alyssia, Zerres, Julia, Denzinger, Markus, Niederberger, David, Wellmann, Sven, Ozkan, Ece, Knorr, Christian, Vogt, Julia E.
Publikováno v:
In Medical Image Analysis January 2024 91
