In this paper, we propose a robust Selective Review Learning (NSRL) framework for NER task with noisy labels. Specifically, we design a Status Loss Function (SLF) which helps the model review the previous knowledge continuously when learning new knowledge, and prevents model from overfitting noisy...
Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need...
Artificial intelligence in ovarian cancer histopathology: a systematic review Article Open access 31 August 2023 Introduction Following recent prominent discoveries in deep learning techniques, wider artificial intelligence (AI) applications have emerged for many sectors, including in healthcare1,2,3. Path...
Table 2. A survey of databases used for cell annotation. 3.1.2. Scoring Method Common scoring methods, like single sample gene set enrichment analysis (ssGSEA, [69]), gene set variation analysis (GSVA, [70]), and Singscore [71], were initially designed for bulk RNA-seq data. The ssG...
Due to the very challenging but high-impact aspects of lifelong learning, a large body of computational approaches have been proposed that take inspiration from the biological factors of learning from the mammalian brain. Humans and other animals excel at learning in a lifelong manner, making the ...
the sources of label noise, theimpact of label noise, the detection of label noise, label noise handling techniques, and theirevaluation. Categorization of both label noise detection methods and handling techniques areprovided.Discussion: From a methodological perspective, we observe that the medical ...
Since Supervised Machine Learning ([Math Processing Error]ML) [1] provides a way to automatically create regression and classification models from labeled datasets, researchers use Supervised [Math Processing Error]ML to model all sorts of phenomena in various fields. Hence, it is vital to stay ...
and its point of use in a machine-learning algorithm. In this survey, we are only concerned with some of these categories. Specifically, in terms of the categories in12, we are interested in implicit or explicit sources of domain-knowledge, represented either as logical or numeric constraints,...
This threatening situation has been exacerbated by the COVID-19 pandemic, because many people have to work from home, resulting in a significant increase in network traffic. According to the 2020 CIRA (Canadian Internet Registration Authority) Cybersecurity Survey, two-thirds of IT workers were ...
we report outcomes from a collaborative data challenge. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects....