Search results for "Stage classification"
showing 4 items of 4 documents
The pathology of bladder cancer: An update on selected issues
2006
OBJECTIVE: To achieve a closer relationship between urologists and pathologists and to use a common language and identical objectives in the pathology of bladder cancer. METHODS AND RESULTS: Special emphasis was given to an analysis of the new World Health Organization (WHO) grading system, to the interpretation of the last Tumour-Nodes-Metastasis staging rules, and to identifying new markers of prognostic significance in clinical practice. A consensus was achieved on the main points. CONCLUSIONS: The 2004 WHO grading system must become acceptable to clinicians, perhaps by a minimal modification of the present terminology. Simple transurethral resection-biopsy should be expressed in terms o…
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance
2022
Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolut…
SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG
2022
In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-conv…
Alleviating Class Imbalance Problem in Automatic Sleep Stage Classification
2022
For real-world automatic sleep-stage classification tasks, various existing deep learning-based models are biased toward the majority with a high proportion. Because of the unique sleep structure, most of the current polysomnography (PSG) datasets suffer an inherent class imbalance problem (CIP), in which the number of each sleep stage is severely unequal. In this study, we first define the class imbalance factor (CIF) to describe the level of CIP quantitatively. Afterward, we propose two balancing methods to alleviate this problem from the dataset quantity and the relationship between the class distribution and the applied model, respectively. The first one is to employ the data augmentati…