Ultrasound image-based diagnosis of malignant thyroid nodule using artificial intelligence
Computer-aided diagnosis systems have been developed to assist doctors in diagnosing
thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly …
thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly …
Mini-COVIDNet: efficient lightweight deep neural network for ultrasound based point-of-care detection of COVID-19
Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for
detection of COVID-19, due to its ease of operation with minimal personal protection …
detection of COVID-19, due to its ease of operation with minimal personal protection …
Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation
Whilst face recognition applications are becoming increasingly prevalent within our daily
lives, leading approaches in the field still suffer from performance bias to the detriment of …
lives, leading approaches in the field still suffer from performance bias to the detriment of …
Convolutional neural networks based focal loss for class imbalance problem: a case study of canine red blood cells morphology classification
Morphologies of red blood cells are normally interpreted by a pathologist. It is time-
consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead …
consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead …
An effective two-stage training scheme for boundary decision of imbalanced samples
Q Xue, S Qiao, G Yang, H Liao, N Han, Y Peng… - International Journal of …, 2024 - Springer
How to categorize imbalanced data is an active research direction in data mining and
machine learning research areas. In order to dynamically reduce the negative influence of …
machine learning research areas. In order to dynamically reduce the negative influence of …
Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models
Confidence calibration is an emerging challenge in real-world decision systems based on
foundations models when used for downstream vision classification tasks. Due to various …
foundations models when used for downstream vision classification tasks. Due to various …
Finding a Suitable Class Distribution for Building Histological Images Datasets Used in Deep Model Training—The Case of Cancer Detection
The class distribution of a training dataset is an important factor which influences the
performance of a deep learning-based system. Understanding the optimal class distribution …
performance of a deep learning-based system. Understanding the optimal class distribution …
Switching: understanding the class-reversed sampling in tail sample memorization
Long-tailed visual recognition poses significant challenges to traditional machine learning
and emerging deep networks due to its inherent class imbalance. Existing reweighting and …
and emerging deep networks due to its inherent class imbalance. Existing reweighting and …
Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets
O Badmos - 2023 - publikationen.bibliothek.kit.edu
Die Qualitätskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die
Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten …
Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten …
Masked Face Recognition Using Transfer Learning Approaches
MAA Mosleh, AM Al-Fakaih, AM Al-Najar… - … on Electronics and …, 2023 - Springer
Face recognition is a subfield of artificial intelligence science that uses different biometric
features of human faces to recognize people. Face recognition systems are widely used due …
features of human faces to recognize people. Face recognition systems are widely used due …