no code implementations • 25 Jun 2023 • Jingxiong Li, Sunyi Zheng, Zhongyi Shui, Shichuan Zhang, Linyi Yang, Yuxuan Sun, Yunlong Zhang, Honglin Li, Yuanxin Ye, Peter M. A. van Ooijen, Kang Li, Lin Yang
This yields a non-trivial reconstruction task, allowing the model to effectively preserve chromosome banding patterns and structure details in the reconstructed results.
Overall, we use the current models to generate pseudo labels for unlabeled images, which are in turn utilized to supervise the models training.
As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation.
Our framework is evaluated on five pathology WSI datasets on various WSI heads.
The numerical tests are conducted on a single corridor and on a local grid network under three traffic demand scenarios of low, medium, and heavy traffic, respectively.
Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities.
Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation.
Data augmentation based methods construct pseudo-healthy images by "pasting" fake lesions on real healthy ones, and a network is trained to predict healthy images in a supervised manner.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method.
In this paper, we propose a novel vessel-mixing based consistency regularization framework, for cross-domain learning in retinal A/V classification.
In this paper, to alleviate this issue, we introduce the semantic space of healthy anatomy in the process of modeling healthy-data distribution.
Second, we further consider that the predictions of target samples belonging to the hard class are vulnerable to perturbations.