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[HTML][HTML] A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions
Data augmentation involves artificially expanding a dataset by applying various
transformations to the existing data. Recent developments in deep learning have advanced …
transformations to the existing data. Recent developments in deep learning have advanced …
[HTML][HTML] Artificial intelligence uncertainty quantification in radiotherapy applications− A sco** review
Background/purpose The use of artificial intelligence (AI) in radiotherapy (RT) is expanding
rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the …
rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the …
CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation
Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that
incorporates progressive changes in patient anatomy into active plan/dose adaption during …
incorporates progressive changes in patient anatomy into active plan/dose adaption during …
Focalunetr: A focal transformer for boundary-aware prostate segmentation using ct images
Computed Tomography (CT) based precise prostate segmentation for treatment planning is
challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft …
challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft …
Multi-stage fully convolutional network for precise prostate segmentation in ultrasound images
Prostate cancer is one of the most commonly diagnosed non-cutaneous malignant tumors
and the sixth major cause of cancer-related death generally found in men globally …
and the sixth major cause of cancer-related death generally found in men globally …
UP-DP: unsupervised prompt learning for data pre-selection with vision-language models
In this study, we investigate the task of data pre-selection, which aims to select instances for
labeling from an unlabeled dataset through a single pass, thereby optimizing performance …
labeling from an unlabeled dataset through a single pass, thereby optimizing performance …
Interpretability-aware vision transformer
Vision Transformers (ViTs) have become prominent models for solving various vision tasks.
However, the interpretability of ViTs has not kept pace with their promising performance …
However, the interpretability of ViTs has not kept pace with their promising performance …
A new architecture combining convolutional and transformer‐based networks for automatic 3D multi‐organ segmentation on CT images
Purpose Deep learning‐based networks have become increasingly popular in the field of
medical image segmentation. The purpose of this research was to develop and optimize a …
medical image segmentation. The purpose of this research was to develop and optimize a …
Harnessing uncertainty in radiotherapy auto-segmentation quality assurance
[7] Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: representing model
uncertainty in deep learning. In: Balcan MF, Weinberger KQ, editors. Proceedings of The …
uncertainty in deep learning. In: Balcan MF, Weinberger KQ, editors. Proceedings of The …
Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists …
M Huet-Dastarac, NMC van Acht, FC Maruccio… - Radiotherapy and …, 2024 - Elsevier
Background and purpose During the ESTRO 2023 physics workshop on “AI for the fully
automated radiotherapy treatment chain”, the topic of deep learning (DL) segmentation was …
automated radiotherapy treatment chain”, the topic of deep learning (DL) segmentation was …