A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
[HTML][HTML] Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram
Artificial Intelligence (AI) use in automated Electrocardiogram (ECG) classification has
continuously attracted the research community's interest, motivated by their promising …
continuously attracted the research community's interest, motivated by their promising …
Benign and malignant skin lesion detection from Melanoma skin cancer images
Skin cancer is the most dangerous and lethal cancer that affects millions of people each
year. The accurate identification of skin cancers can not be accomplished without expert …
year. The accurate identification of skin cancers can not be accomplished without expert …
An optimized uncertainty-aware training framework for neural networks
P Tabarisaadi, A Khosravi, S Nahavandi… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Uncertainty quantification (UQ) for predictions generated by neural networks (NNs) is of vital
importance in safety-critical applications. An ideal model is supposed to generate low …
importance in safety-critical applications. An ideal model is supposed to generate low …
[HTML][HTML] Hierarchical agglomerative clustering-based skin lesion detection with region based neural networks classification
MVS Ramprasad, SSV Nagesh, V Sahith… - Measurement …, 2023 - Elsevier
The skin cancer disease is widely spreading in the world due to abnormal increment of
radiation. The segmentation of images is a crucial yet challenging aspect of the image …
radiation. The segmentation of images is a crucial yet challenging aspect of the image …
Unic-net: Uncertainty aware involution-convolution hybrid network for two-level disease identification
Convolution is commonly used in deep learning models for image classification problems,
and its primary purpose is to retrieve representations in the spatial domain that are …
and its primary purpose is to retrieve representations in the spatial domain that are …
Uncertainty-Aware Deep Learning for Segmenting Ultrasound Images of Breast Tumours
Precise image segmentation is one of the dominant factors in disease diagnosis. A typical
application is the segmentation of breast ultrasound images, allowing radiologists to suggest …
application is the segmentation of breast ultrasound images, allowing radiologists to suggest …
Development of Dermatological Lesion Detection System Using EfficientNet with Fairness Evaluation
M Khanam, E Kumar - International Conference On Innovative Computing …, 2024 - Springer
The most common diseases in the world are skin problems because of hereditary features
and environmental factors. It can be very challenging to distinguish between different skin …
and environmental factors. It can be very challenging to distinguish between different skin …
Skin Cancer Identification Using Deep Learning Technique
GK Gautam, S Singh, A Singh - 2024 International Conference …, 2024 - ieeexplore.ieee.org
The most common diseases in the world are skin problems because of hereditary features
and environmental factors. It frequently suffers from both common and uncommon ailments …
and environmental factors. It frequently suffers from both common and uncommon ailments …
BSM loss: A superior way in modeling aleatory uncertainty of fine_grained classification
S Ge, K Yuan, M Han, D Sun, H Zhang, Q Ye - arXiv preprint arXiv …, 2022 - arxiv.org
Artificial intelligence (AI)-assisted method had received much attention in the risk field such
as disease diagnosis. Different from the classification of disease types, it is a fine-grained …
as disease diagnosis. Different from the classification of disease types, it is a fine-grained …