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Sculpting Efficiency: Pruning Medical Imaging Models for On-Device Inference
Sudarshan Sreeram, Bernhard Kainz
Leveraging ML advancements to augment healthcare systems can improve patient outcomes. Yet, uninformed engineering decisions in early-stage research inadvertently hinder the feasib…
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
Berke Doga Basaran, Weitong Zhang, Mengyun Qiao +3
Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spat…
Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction
Qiang Ma, Liu Li, Vanessa Kyriakopoulou +4
Cortical surface reconstruction plays a fundamental role in modeling the rapid brain development during the perinatal period. In this work, we propose Conditional Temporal Attentio…
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
Matthew Baugh, Jeremy Tan, Johanna P. Müller +3
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…
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models
Matthew Baugh, James Batten, Johanna P. Müller +1
This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the performance of the WINCLIP f…
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting
Mischa Dombrowski, Bernhard Kainz
Recent advances in score-based generative models have led to a huge spike in the development of downstream applications using generative models ranging from data augmentation over…
Realistic Data Enrichment for Robust Image Segmentation in Histopathology
Sarah Cechnicka, James Ball, Hadrien Reynaud +3
Poor performance of quantitative analysis in histopathological Whole Slide Images (WSI) has been a significant obstacle in clinical practice. Annotating large-scale WSIs manually i…
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
Mischa Dombrowski, Hadrien Reynaud, Johanna P. Müller +2
Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-train…
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Johanna P. Müller, Matthew Baugh, Jeremy Tan +2
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class…
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
Hadrien Reynaud, Mengyun Qiao, Mischa Dombrowski +5
Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, ca…