|
|
|
|
|
Daniel Rusche, Nils Englert, Marlen Runz, Svetlana Hetjens, Cord Langner, Timo Gaiser and Cleo-Aron Weis
Background: In this study focusing on colorectal carcinoma (CRC), we address the imperative task of predicting post-surgery treatment needs by identifying crucial tumor features within whole slide images of solid tumors, analogous to locating a needle in...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ziyang Wang and Irina Voiculescu
Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clinical ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Haoxiang Shi, Jun Ai, Jingyu Liu and Jiaxi Xu
Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by genera...
ver más
|
|
|
|
|
|
|
|
|
|
|
Qingji Guan, Qinrun Chen and Yaping Huang
Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jialin Shi, Chenyi Guo and Ji Wu
Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effect...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yaojie Zhang, Huahu Xu, Junsheng Xiao and Minjie Bian
The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most ad...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xuxin Chen and Xinli Huang
Distant supervision for relation extraction (DSRE) automatically acquires large-scale annotated data by aligning the corpus with the knowledge base, which dramatically reduces the cost of manual annotation. However, this technique is plagued by noisy dat...
ver más
|
|
|
|
|
|
|
|
|
|
|
Stefan Helmstetter and Heiko Paulheim
The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Divya Padmanabhan, Satyanath Bhat, Shirish Shevade and Y. Narahari
-
|
|
|
|
|
|