Ai-based anomaly detection for clinical-grade histopathological diagnostics
Background While previous studies of artificial intelligence (AI) have shown its potential for
diagnosing diseases using imaging data, clinical implementation lags behind. AI models …
diagnosing diseases using imaging data, clinical implementation lags behind. AI models …
Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs
Background Histopathology is a gold standard for cancer diagnosis. It involves extracting
tissue specimens from suspicious areas to prepare a glass slide for a microscopic …
tissue specimens from suspicious areas to prepare a glass slide for a microscopic …
Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation
Recent surface anomaly detection methods rely on pretrained backbone networks for
efficient anomaly detection. On standard RGB anomaly detection benchmarks these …
efficient anomaly detection. On standard RGB anomaly detection benchmarks these …
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
Radiography imaging protocols focus on particular body regions, therefore producing
images of great similarity and yielding recurrent anatomical structures across patients …
images of great similarity and yielding recurrent anatomical structures across patients …
Out-of-distribution detection in digital pathology: Do foundation models bring the end to reconstruction-based approaches?
M Pocevičiūtė, Y Ding, R Bromée, G Eilertsen - Computers in Biology and …, 2024 - Elsevier
Artificial intelligence (AI) has shown promising results for computational pathology tasks.
However, one of the limitations in clinical practice is that these algorithms are optimised for …
However, one of the limitations in clinical practice is that these algorithms are optimised for …
[HTML][HTML] SaltGAN: A feature-infused and loss-controlled generative adversarial network with preserved checkpoints for evolving histopathology images
ON Oyelade, H Wang, SA Adewuyi - Biomedical Signal Processing and …, 2024 - Elsevier
The use of natural phenomena as inspiration to address real-life problems has become an
increasingly popular research approach. In the medical domain, generative adversarial …
increasingly popular research approach. In the medical domain, generative adversarial …
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
Stain normalization algorithms aim to transform the color and intensity characteristics of a
source multi-gigapixel histology image to match those of a target image, mitigating …
source multi-gigapixel histology image to match those of a target image, mitigating …
Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Models
E Demircan-Tureyen, F Lucka… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent works demonstrate the effectiveness of diffusion models as unsupervised solvers for
inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited …
inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited …
Differentiable Score-Based Likelihoods: Learning CT Motion Compensation from Clean Images
Motion artifacts can compromise the diagnostic value of computed tomography (CT) images.
Motion correction approaches require a per-scan estimation of patient-specific motion …
Motion correction approaches require a per-scan estimation of patient-specific motion …
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment
Digital pathology has advanced significantly over the last decade, with Whole Slide Images
(WSIs) encompassing vast amounts of data essential for accurate disease diagnosis. High …
(WSIs) encompassing vast amounts of data essential for accurate disease diagnosis. High …