Efficient-capsnet: Capsule network with self-attention routing
Deep convolutional neural networks, assisted by architectural design strategies, make
extensive use of data augmentation techniques and layers with a high number of feature …
extensive use of data augmentation techniques and layers with a high number of feature …
Radiologist-level covid-19 detection using ct scans with detail-oriented capsule networks
Radiographic images offer an alternative method for the rapid screening and monitoring of
Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of …
Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of …
Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks
Mapping biological processes in brain tissues requires piecing together numerous
histological observations of multiple tissue samples. We present a direct method that …
histological observations of multiple tissue samples. We present a direct method that …
Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis
T Han, R Ma, J Zheng - Measurement, 2021 - Elsevier
For the application of deep learning in the field of fault diagnosis, its recognition accuracy is
limited by the size and quality of the training samples, such as small size samples, low …
limited by the size and quality of the training samples, such as small size samples, low …
[HTML][HTML] DA-CapsNet: dual attention mechanism capsule network
W Huang, F Zhou - Scientific Reports, 2020 - nature.com
A capsule network (CapsNet) is a recently proposed neural network model with a new
structure. The purpose of CapsNet is to form activation capsules. In this paper, our team …
structure. The purpose of CapsNet is to form activation capsules. In this paper, our team …
Risk-aware machine learning classifier for skin lesion diagnosis
Knowing when a machine learning system is not confident about its prediction is crucial in
medical domains where safety is critical. Ideally, a machine learning algorithm should make …
medical domains where safety is critical. Ideally, a machine learning algorithm should make …
Kidney level lupus nephritis classification using uncertainty guided Bayesian convolutional neural networks
The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-
observer agreement, with misdiagnosis being associated with increased patient morbidity …
observer agreement, with misdiagnosis being associated with increased patient morbidity …
Encoding visual attributes in capsules for explainable medical diagnoses
Convolutional neural network based systems have largely failed to be adopted in many high-
risk application areas, including healthcare, military, security, transportation, finance, and …
risk application areas, including healthcare, military, security, transportation, finance, and …
Memory-augmented capsule network for adaptable lung nodule classification
Computer-aided diagnosis (CAD) systems must constantly cope with the perpetual changes
in data distribution caused by different sensing technologies, imaging protocols, and patient …
in data distribution caused by different sensing technologies, imaging protocols, and patient …
Scanning ion conductance microscopy revealed cisplatin-induced morphological changes related to apoptosis in single adenocarcinoma cells
Y Muhammed, RA Lazenby - Analytical Methods, 2024 - pubs.rsc.org
The studies of drug-induced apoptosis play a vital role in the identification of potential drugs
that could treat diseases such as cancer. Alterations in the native morphology of cancer cells …
that could treat diseases such as cancer. Alterations in the native morphology of cancer cells …