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Found 7 papers, 5 papers with code
Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks
We introduce a fast Self-adapting Forward-Forward Network (SaFF-Net) for medical imaging analysis, mitigating power consumption and resource limitations, which currently primarily stem from the prevalent reliance on back-propagation for model training and fine-tuning.
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis
Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance.
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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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.
