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pro vyhledávání: '"Kainz, A"'
Large Language Models (LLMs) often produce outputs that -- though plausible -- can lack consistency and reliability, particularly in ambiguous or complex scenarios. Challenges arise from ensuring that outputs align with both factual correctness and h
Externí odkaz:
http://arxiv.org/abs/2410.01064
Autor:
Prenner, Andrea, Kainz, Bernhard
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facil
Externí odkaz:
http://arxiv.org/abs/2409.17800
Autor:
Reynaud, Hadrien, Baugh, Matthew, Dombrowski, Mischa, Cechnicka, Sarah, Meng, Qingjie, Kainz, Bernhard
We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos. We achieve this by integrating two diffusion models: a Latent Image Diffusion Model (LIDM) trained on images and a
Externí odkaz:
http://arxiv.org/abs/2409.14149
Autor:
Li, Liu, Wang, Hanchun, Baugh, Matthew, Ma, Qiang, Zhang, Weitong, Ouyang, Cheng, Rueckert, Daniel, Kainz, Bernhard
Although existing medical image segmentation methods provide impressive pixel-wise accuracy, they often neglect topological correctness, making their segmentations unusable for many downstream tasks. One option is to retrain such models whilst includ
Externí odkaz:
http://arxiv.org/abs/2409.09796
While deep learning techniques have proven successful in image-related tasks, the exponentially increased data storage and computation costs become a significant challenge. Dataset distillation addresses these challenges by synthesizing only a few im
Externí odkaz:
http://arxiv.org/abs/2409.03929
Autor:
Cechnicka, Sarah, Ball, James, Baugh, Matthew, Reynaud, Hadrien, Simmonds, Naomi, Smith, Andrew P. T., Horsfield, Catherine, Roufosse, Candice, Kainz, Bernhard
Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI). However, existing generative methods fail to consistently represent the hierarchical structure of WSIs due
Externí odkaz:
http://arxiv.org/abs/2407.13277
Autor:
Marimont, Sergio Naval, Siomos, Vasilis, Baugh, Matthew, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by gener
Externí odkaz:
http://arxiv.org/abs/2407.06635
Autor:
Müller, Johanna P., Kainz, Bernhard
We introduce a fast Self-adapting Forward-Forward Network (SaFF-Net) for medical imaging analysis, mitigating power consumption and resource limitations, which currently primarily stem from the prevalent reliance on back-propagation for model trainin
Externí odkaz:
http://arxiv.org/abs/2406.14038
Inverse problems describe the process of estimating the causal factors from a set of measurements or data. Mapping of often incomplete or degraded data to parameters is ill-posed, thus data-driven iterative solutions are required, for example when re
Externí odkaz:
http://arxiv.org/abs/2406.13652
Autor:
Li, Zhe, Kainz, Bernhard
Histopathology can help clinicians make accurate diagnoses, determine disease prognosis, and plan appropriate treatment strategies. As deep learning techniques prove successful in the medical domain, the primary challenges become limited data availab
Externí odkaz:
http://arxiv.org/abs/2406.13536