Zobrazeno 1 - 10
of 24 388
pro vyhledávání: '"Marr, A"'
Autor:
Koch, Valentin, Bauer, Sabine, Luppberger, Valerio, Joner, Michael, Schunkert, Heribert, Schnabel, Julia A., von Scheidt, Moritz, Marr, Carsten
Background: The integration of multi-stain histopathology images through deep learning poses a significant challenge in digital histopathology. Current multi-modal approaches struggle with data heterogeneity and missing data. This study aims to overc
Externí odkaz:
http://arxiv.org/abs/2409.17775
Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia. By combining morphological and transcriptomic data, we can gain insights into cellular functions and trajectories
Externí odkaz:
http://arxiv.org/abs/2408.06720
Autor:
Naik, Atharva, Zhang, Kexun, Robinson, Nathaniel, Mysore, Aravind, Marr, Clayton, Byrnes, Hong Sng Rebecca, Cai, Anna, Chang, Kalvin, Mortensen, David
Historical linguists have long written a kind of incompletely formalized ''program'' that converts reconstructed words in an ancestor language into words in one of its attested descendants that consist of a series of ordered string rewrite functions
Externí odkaz:
http://arxiv.org/abs/2406.12725
Diagnosis of hematological malignancies depends on accurate identification of white blood cells in peripheral blood smears. Deep learning techniques are emerging as a viable solution to scale and optimize this process by automatic cell classification
Externí odkaz:
http://arxiv.org/abs/2404.05584
Autor:
Koch, Valentin, Wagner, Sophia J., Kazeminia, Salome, Sancar, Ece, Hehr, Matthias, Schnabel, Julia, Peng, Tingying, Marr, Carsten
In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears. However, clinical adoption of compu
Externí odkaz:
http://arxiv.org/abs/2404.05022
Autor:
Sun, Xudong, Feistner, Carla, Gossmann, Alexej, Schwarz, George, Umer, Rao Muhammad, Beer, Lisa, Rockenschaub, Patrick, Shrestha, Rahul Babu, Gruber, Armin, Chen, Nutan, Boushehri, Sayedali Shetab, Buettner, Florian, Marr, Carsten
Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniques address this problem by adding a domain invariant regularization l
Externí odkaz:
http://arxiv.org/abs/2403.14356
Autor:
Sun, Xudong, Chen, Nutan, Gossmann, Alexej, Xing, Yu, Feistner, Carla, Dorigatt, Emilio, Drost, Felix, Scarcella, Daniele, Beer, Lisa, Marr, Carsten
We address the online combinatorial choice of weight multipliers for multi-objective optimization of many loss terms parameterized by neural works via a probabilistic graphical model (PGM) for the joint model parameter and multiplier evolution proces
Externí odkaz:
http://arxiv.org/abs/2403.13728
Automated disease diagnosis using medical image analysis relies on deep learning, often requiring large labeled datasets for supervised model training. Diseases like Acute Myeloid Leukemia (AML) pose challenges due to scarce and costly annotations on
Externí odkaz:
http://arxiv.org/abs/2403.05379
Autor:
Roth, Benedikt, Koch, Valentin, Wagner, Sophia J., Schnabel, Julia A., Marr, Carsten, Peng, Tingying
To handle the large scale of whole slide images in computational pathology, most approaches first tessellate the images into smaller patches, extract features from these patches, and finally aggregate the feature vectors with weakly-supervised learni
Externí odkaz:
http://arxiv.org/abs/2401.04720
Publikováno v:
The Art, Science, and Engineering of Programming, 2024, Vol. 8, Issue 2, Article 5
Object-oriented languages often use virtual machines (VMs) that provide mechanisms such as just-in-time (JIT) compilation and garbage collection (GC). These VM components are typically implemented in a separate layer, isolating them from the applicat
Externí odkaz:
http://arxiv.org/abs/2312.16973