Zobrazeno 1 - 10
of 1 558
pro vyhledávání: '"A. Tsaftaris"'
Autor:
Sun, Jinghan, Wei, Dong, Xu, Zhe, Lu, Donghuan, Liu, Hong, Wang, Hong, Tsaftaris, Sotirios A., McDonagh, Steven, Zheng, Yefeng, Wang, Liansheng
Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the
Externí odkaz:
http://arxiv.org/abs/2412.13599
In this work, we present compelling evidence that controlling model capacity during fine-tuning can effectively mitigate memorization in diffusion models. Specifically, we demonstrate that adopting Parameter-Efficient Fine-Tuning (PEFT) within the pr
Externí odkaz:
http://arxiv.org/abs/2410.22149
Autor:
Liu, Jingshuai, Andres, Alain, Jiang, Yonghang, Luo, Xichun, Shu, Wenmiao, Tsaftaris, Sotirios A.
Surgical robot task automation has recently attracted great attention due to its potential to benefit both surgeons and patients. Reinforcement learning (RL) based approaches have demonstrated promising ability to provide solutions to automated surgi
Externí odkaz:
http://arxiv.org/abs/2409.02724
Autor:
Xue, Yuyang, Yan, Junyu, Dutt, Raman, Haider, Fasih, Liu, Jingshuai, McDonagh, Steven, Tsaftaris, Sotirios A.
Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness in machine learning require a substantial amount of training data and
Externí odkaz:
http://arxiv.org/abs/2408.06890
Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one. For instance, a classifier trained on chest X-ray (CXR) images from one hospital may not generalize to images from another h
Externí odkaz:
http://arxiv.org/abs/2408.04949
Leaf instance segmentation is a challenging multi-instance segmentation task, aiming to separate and delineate each leaf in an image of a plant. Accurate segmentation of each leaf is crucial for plant-related applications such as the fine-grained mon
Externí odkaz:
http://arxiv.org/abs/2406.17109
Despite the high diagnostic accuracy of Magnetic Resonance Imaging (MRI), using MRI as a Point-of-Care (POC) disease identification tool poses significant accessibility challenges due to the use of high magnetic field strength and lengthy acquisition
Externí odkaz:
http://arxiv.org/abs/2406.16754
Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization, raising privacy, ethical, and legal concerns, particularly in sensitive domains such as medical imaging. We hypothesi
Externí odkaz:
http://arxiv.org/abs/2405.19458
Machine unlearning is a promising paradigm for removing unwanted data samples from a trained model, towards ensuring compliance with privacy regulations and limiting harmful biases. Although unlearning has been shown in, e.g., classification and reco
Externí odkaz:
http://arxiv.org/abs/2405.15517
Localizing the exact pathological regions in a given medical scan is an important imaging problem that requires a large amount of bounding box ground truth annotations to be accurately solved. However, there exist alternative, potentially weaker, for
Externí odkaz:
http://arxiv.org/abs/2404.12920