330 citations · 333 across the 8 of their papers we have counts for
8 papers
What Matters is the Prompt: Prompt Sensitivity and Prompt Generation in Foundation Models for Lung Nodule Segmentation
Jorge F. Lazo, Xixi Liu, Andreas Hallqvist +5
Lung nodule segmentation in computed tomography is essential for extracting clinically relevant information for lung cancer assessment and treatment planning. Foundation models hav…
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
Sofie Allgöwer, Mikael Johansson, Andreas Hallqvist +4
Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging co…
CEVAR: Centerline Embedding Extraction for Endovascular Aneurysm Repair
Roman Naeem, Timo Niiniskorpi, Charlotte Sandström +6
Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft sealing zones. Structured CT revi…
Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels
Victor Wåhlstrand, Jennifer Alvén, Ida Häggström
We present a framework to take advantage of existing labels at inference, called \textit{exemplars}, in order to improve the performance of object detection in medical images. The…
Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification
Victor Wåhlstrand Skärström, Lisa Johansson, Jennifer Alvén +2
We present a novel method for explainable vertebral fracture assessment (XVFA) in low-dose radiographs using deep neural networks, incorporating vertebra detection and keypoint loc…
H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma
Gabriele Campanella, David Ho, Ida Häggström +4
Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most prevalent form of lung cancer. EGFR positive lung adenocarcinomas have been show…
Depth resolved pencil beam radiography using AI -- a proof of principle study
Ida Häggström, Lukas M. Carter, Thomas J. Fuchs +1
AIMS: Clinical radiographic imaging is seated upon the principle of differential keV photon transmission through an object. At clinical x-ray energies the scattering of photons cau…
DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem
Ida Häggström, C. Ross Schmidtlein, Gabriele Campanella +1
Positron emission tomography (PET) is a cornerstone of modern radiology. The ability to detect cancer and metastases in whole body scans fundamentally changed cancer diagnosis and…