[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …
respect to the quantity of high-performing solutions reported in the literature. End users are …
Uncertainty-aware deep learning in healthcare: a scoping review
Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment
could be earned by conveying model certainty, or the probability that a given model output is …
could be earned by conveying model certainty, or the probability that a given model output is …
Object pose estimation with statistical guarantees: Conformal keypoint detection and geometric uncertainty propagation
The two-stage object pose estimation paradigm first detects semantic keypoints on the
image and then estimates the 6D pose by minimizing reprojection errors. Despite performing …
image and then estimates the 6D pose by minimizing reprojection errors. Despite performing …
Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
Unreliable predictions can occur when an artificial intelligence (AI) system is presented with
data it has not been exposed to during training. We demonstrate the use of conformal …
data it has not been exposed to during training. We demonstrate the use of conformal …
Self-supervised transfer learning based on domain adaptation for benign-malignant lung nodule classification on thoracic CT
H Huang, R Wu, Y Li, C Peng - IEEE Journal of Biomedical and …, 2022 - ieeexplore.ieee.org
The spatial heterogeneity is an important indicator of the malignancy of lung nodules in lung
cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule …
cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule …
[HTML][HTML] Combining molecular and cell painting image data for mechanism of action prediction
G Tian, PJ Harrison, AP Sreenivasan… - Artificial Intelligence in …, 2023 - Elsevier
The mechanism of action (MoA) of a compound describes the biological interaction through
which it produces a pharmacological effect. Multiple data sources can be used for the …
which it produces a pharmacological effect. Multiple data sources can be used for the …
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Abstract Machine learning is increasingly applied to Earth Observation (EO) data to obtain
datasets that contribute towards international accords. However, these datasets contain …
datasets that contribute towards international accords. However, these datasets contain …
Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction
A promise of genomics in precision medicine is to provide individualized genetic risk
predictions. Polygenic risk scores (PRS), computed by aggregating effects from many …
predictions. Polygenic risk scores (PRS), computed by aggregating effects from many …
Colon tissues classification and localization in whole slide images using deep learning
Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early
diagnosis of colon cancer not only reduces mortality but also reduces the burden related to …
diagnosis of colon cancer not only reduces mortality but also reduces the burden related to …
Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
We propose a post-hoc computationally lightweight method to quantify predictive uncertainty
in semantic image segmentation. Our approach uses conformal prediction to generate …
in semantic image segmentation. Our approach uses conformal prediction to generate …