Zobrazeno 1 - 10
of 420
pro vyhledávání: '"Pluim, Josien P. W."'
The influence of bias in datasets on the fairness of model predictions is a topic of ongoing research in various fields. We evaluate the performance of skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training
Externí odkaz:
http://arxiv.org/abs/2407.17543
The Segment Anything Model (SAM) marks a significant advancement in segmentation models, offering robust zero-shot abilities and dynamic prompting. However, existing medical SAMs are not suitable for the multi-scale nature of whole-slide images (WSIs
Externí odkaz:
http://arxiv.org/abs/2403.09257
Out-of-distribution (OOD) detection is crucial for the safety and reliability of artificial intelligence algorithms, especially in the medical domain. In the context of the Medical OOD (MOOD) detection challenge 2023, we propose a pipeline that combi
Externí odkaz:
http://arxiv.org/abs/2310.08654
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentati
Externí odkaz:
http://arxiv.org/abs/2304.13476
Autor:
Jiao, Yiping, van der Laak, Jeroen, Albarqouni, Shadi, Li, Zhang, Tan, Tao, Bhalerao, Abhir, Ma, Jiabo, Sun, Jiamei, Pocock, Johnathan, Pluim, Josien P. W., Koohbanani, Navid Alemi, Bashir, Raja Muhammad Saad, Raza, Shan E Ahmed, Liu, Sibo, Graham, Simon, Wetstein, Suzanne, Khurram, Syed Ali, Watson, Thomas, Rajpoot, Nasir, Veta, Mitko, Ciompi, Francesco
We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells,
Externí odkaz:
http://arxiv.org/abs/2301.06304
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentati
Externí odkaz:
http://arxiv.org/abs/2207.12872
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
Deep learning techniques show success in detecting objects in medical images, but still suffer from false-positive predictions that may hinder accurate diagnosis. The estimated uncertainty of the neural network output has been used to flag incorrect
Externí odkaz:
http://arxiv.org/abs/2206.10911
We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation
Externí odkaz:
http://arxiv.org/abs/2107.12734
Autor:
Heslinga, Friso G., Lucassen, Ruben T., Berg, Myrthe A. van den, van der Hoek, Luuk, Pluim, Josien P. W., Cabrerizo, Javier, Alberti, Mark, Veta, Mitko
Corneal thickness (pachymetry) maps can be used to monitor restoration of corneal endothelial function, for example after Descemet's membrane endothelial keratoplasty (DMEK). Automated delineation of the corneal interfaces in anterior segment optical
Externí odkaz:
http://arxiv.org/abs/2102.07846
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network usin
Externí odkaz:
http://arxiv.org/abs/2101.04386