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Found 11 papers, 7 papers with code
URCDM: Ultra-Resolution Image Synthesis in Histopathology
Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI).
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free.
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score.
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in their training.
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models
This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge.
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-trained models using domain-specific text-to-image datasets.
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis.
Out of Distribution (OOD) Detection
Self-Supervised Anomaly Detection
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Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models
Curating datasets for object segmentation is a difficult task.
Zero-Shot Object Segmentation through Concept Distillation from Generative Image Foundation Models
Curating datasets for object segmentation is a difficult task.
Adnexal Mass Segmentation with Ultrasound Data Synthesis
Ovarian cancer is the most lethal gynaecological malignancy.
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
It is also difficult to assess whether a task generalises well for universal anomaly detection, as they are often only tested on a limited range of anomalies.
