Essays about: "monte carlo dropout"
Showing result 1 - 5 of 19 essays containing the words monte carlo dropout.
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1. Uncertainty Estimation in Radiation Dose Prediction U-Net
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The ability to quantify uncertainties associated with neural network predictions is crucial when they are relied upon in decision-making processes, especially in safety-critical applications like radiation therapy. In this paper, a single-model estimator of both epistemic and aleatoric uncertainties in a regression 3D U-net used for radiation dose prediction is presented. READ MORE
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2. Multiclass Brain Tumour Tissue Classification on Histopathology Images Using Vision Transformers
University essay from Linköpings universitet/Statistik och maskininlärningAbstract : Histopathology refers to inspecting and analysing tissue samples under a microscope to identify and examine signs of diseases. The manual investigation procedure of histology slides by pathologists is time-consuming and susceptible to misconceptions. READ MORE
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3. Clinical Assessment of Deep Learning-Based Uncertainty Maps in Lung Cancer Segmentation
University essay from KTH/Skolan för kemi, bioteknologi och hälsa (CBH)Abstract : Prior to radiation therapy planning, tumours and organs at risk need to be delineated. In recent years, deep learning models have opened the possibility of automating the contouring process, speeding up the procedures and helping clinicians. READ MORE
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4. Real-time uncertainty estimation for deep learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Modern deep neural networks do not produce well calibrated estimates of their own uncertainty, unless specific uncertainty estimation techniques are applied. Common uncertainty estimation techniques such as Deep Ensembles and Monte Carlo Dropout necessitate multiple forward pass evaluations for each input sample, making them too slow for real-time use. READ MORE
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5. Anomaly or not Anomaly, that is the Question of Uncertainty : Investigating the relation between model uncertainty and anomalies using a recurrent autoencoder approach to market time series
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : Knowing when one does not know is crucial in decision making. By estimating uncertainties humans can recognize novelty both by intuition and reason, but most AI systems lack this self-reflective ability. In anomaly detection, a common approach is to train a model to learn the distinction between some notion of normal and some notion of anomalies. READ MORE
