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pro vyhledávání: '"Bathula, Deepti. R."'
In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by providing insights into the model
Externí odkaz:
http://arxiv.org/abs/2309.13132
Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences, like Visual (V), Auditory (A), Read/Write (R), and Kinesthetic (K), for
Externí odkaz:
http://arxiv.org/abs/2212.02931
Prototypical network (PN) is a simple yet effective few shot learning strategy. It is a metric-based meta-learning technique where classification is performed by computing Euclidean distances to prototypical representations of each class. Conventiona
Externí odkaz:
http://arxiv.org/abs/2208.09345
Prototypical network (PN) is a simple yet effective few shot learning strategy. It is a metric-based meta-learning technique where classification is performed by computing Euclidean distances to prototypical representations of each class. Conventiona
Externí odkaz:
http://arxiv.org/abs/2111.00698
Autor:
Niyaz, Usma, Bathula, Deepti R.
Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network. In contrast, Mutual Learning (ML) provides an alternative strateg
Externí odkaz:
http://arxiv.org/abs/2110.11023
In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be att
Externí odkaz:
http://arxiv.org/abs/2110.11012
Medical imaging datasets are inherently high dimensional with large variability and low sample sizes that limit the effectiveness of deep learning algorithms. Recently, generative adversarial networks (GANs) with the ability to synthesize realist ima
Externí odkaz:
http://arxiv.org/abs/2108.02160
Autor:
Bagchi, Subhranil, Bathula, Deepti R.
Different categories of visual stimuli activate different responses in the human brain. These signals can be captured with EEG for utilization in applications such as Brain-Computer Interface (BCI). However, accurate classification of single-trial da
Externí odkaz:
http://arxiv.org/abs/2107.03983
Publikováno v:
In Computers in Biology and Medicine January 2024 168
Limited availability of annotated medical imaging data poses a challenge for deep learning algorithms. Although transfer learning minimizes this hurdle in general, knowledge transfer across disparate domains is shown to be less effective. On the othe
Externí odkaz:
http://arxiv.org/abs/2005.11746