AIMC Topic: Uncertainty
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A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.
Journal of imaging informatics in medicine
Sep 23, 2024
This study developed and validated a deep learning-based diagnostic model with uncertainty estimation to aid radiologists in the preoperative differentiation of pathological subtypes of renal cell carcinoma (RCC) based on computed tomography (CT) ima...
Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.
Journal of imaging informatics in medicine
Sep 23, 2024
Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. M...
Deep evidential learning for radiotherapy dose prediction.
Computers in biology and medicine
Sep 23, 2024
BACKGROUND: As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an un...
Deep learning-based statistical robustness evaluation of intensity-modulated proton therapy for head and neck cancer.
Physics in medicine and biology
Sep 20, 2024
. Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice wh...
Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Sep 17, 2024
BACKGROUND/PURPOSE: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. ...
Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department.
Social science & medicine (1982)
Sep 6, 2024
The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have star...
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction Using the Local Lipschitz.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Accurate image reconstruction is at the heart of diagnostics in medical imaging. Supervised deep learning-based approaches have been investigated for solving inverse problems including image reconstruction. However, these trained models encounter uns...
Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies.
Scientific reports
Sep 4, 2024
This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertai...
Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation.
Biomedical physics & engineering express
Aug 30, 2024
Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and consistent but may produce unr...
Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aug 29, 2024
This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly d...
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