Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal
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Published in
Nature methods
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, w...
Reinke, Annika; Tizabi, Minu D.; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Kavur, A. Emre; Rädsch, Tim; Sudre, Carole H.; Acion, Laura; Antonelli, Michela;
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Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. ...
Reinke, Annika Tizabi, Minu D Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A Emre Rädsch, Tim Sudre, Carole H Acion, Laura Antonelli, Michela
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Published in
Nature methods
Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and l...
Wu, Zongwei Zhou, Zhuyun Allibert, Guillaume Stolz, Christophe Demonceaux, Cédric Ma, Chao
Fusing geometric cues with visual appearance is an imperative theme for RGB-D indoor semantic segmentation. Existing methods commonly adopt convolutional modules to aggregate multi-modal features, paying little attention to explicitly leveraging the long-range dependencies in feature fusion. Therefore, it is challenging for existing methods to accu...
Rachakonda, Sai Swaroop
Background: In recent years, computer vision has undergone significant advancements, revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to this transformation is Simultaneous Localization and Mapping(SLAM), a fundamental technology that allows machines to navigate and interactintelligently with their surroundings...
Gella, Blake Bagnol
We propose a method to infer semantic segmentation maps from images captured under adverse weather conditions. We begin by examining existing models on images degraded by weather conditions such as rain, fog, or snow, and found that they exhibit a large performance drop as compared to those captured under clear weather. To control for changes in sc...
Skog, Mikael
Autonomous driving technology is progressively being adopted on public roads and in industrial settings such as mines. With autonomous vehicles expected to increasingly displace human drivers, it is crucial that their adoption does not risk the safety of people near the vehicles. Adverse weather conditions, such as precipitation, may impede the abi...
Arhant, Yoann Tellez, Olga Lopera Neyt, Xavier Pizurica, Aleksandra
This paper addresses a critical issue in seabed characterization with deep learning semantic segmentation using high-resolution Synthetic Aperture Sonar (SAS) data, that we call Catastrophic Receptive Field Overflow (CRFO). We propose novel methods, including Mosaic Augmentation and Homogeneous Patch Rejection, to (1) effectively mitigate CRFO and ...
Challa, Venkata Vamsi
Background:The dynamic field of computer vision and artificial intelligence has continually evolved, pushing the boundaries in areas like semantic segmentation andenvironmental recognition, pivotal for indoor scene analysis. This research investigates the integration of these two technologies, examining their synergy and implicayions for enhancing ...
horváth, a rajki, f ascoli, a tetzlaff, r
We present simulation results of a deep cellular neural network leveraging memristive dynamics to classify and segment images from commonly examined datasets. We have investigated the use of both volatile (NbOx-Mott) and nonvolatile (TaOx) memristive devices in memristive cellular neural networks. We simulated deep neural networks using these devic...
