In this work a new approach is proposed that considers all possible segmentations resulting from an ambiguous segmentation simultaneously in only one relaxation ...

The final objective is to find image segments that can be interpreted (classified) such that their interpretations do not conflict with interpretations given to ...

Nov 28, 2022 · Brief Review — A Probabilistic U-Net for Segmentation of Ambiguous Images. Probabilistic U-Net, Using Conditional Variational Autoencoder ...

Oct 22, 2024 · In this study, the authors present a new approach to segment and classify moving objects in video sequences by combining an unsupervised ...

Mar 16, 2024 · We propose a novel module called the Uncertainty-aware Adapter, which efficiently fine-tuning SAM for uncertainty-aware medical image segmentation.

The proposed method is evaluated on two different datasets - a lung abnormalities dataset in which each image has 4 associated ground-truth segmentations from ...

May 13, 2024 · ... image analysis, with a focus on developing deep learning techniques to make healthcare more affordable and accessible globally. Her specific ...

Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the ...

Nov 5, 2024 · The paper presents a novel approach to handling the inherent ambiguities in the SAM used for image segmentation. SAM, despite its robustness ...

Providing only pixel-wise probabilities ignores all co-variances between the pixels, which makes a subsequent analysis much more difficult if not impossible. If ...