Frontiers

Automated Image Quantitation for Cancer Response Assessment and Clinical Trial Endpoints

Cancer imaging is moving from a purely diagnostic tool to a source of measurable, reproducible data that can guide treatment decisions and trial outcomes.
C&EN

Structure–Stability Relationships in Pt-Alloy Nanoparticles Using Identical-Location Four-Dimensional Scanning Transmission Electron Microscopy and Unsupervised Machine Learning

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Nanoparticulate electrocatalysts for the oxygen reduction reaction are structurally ...
IEEE

Contrastive Registration for Unsupervised Medical Image Segmentation

Abstract: Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using ...
GitHub

Cut and Learn for Unsupervised Image & Video Object Detection and Instance Segmentation

We propose MaskCut approach to generate pseudo-masks for multiple objects in an image. CutLER can learn unsupervised object detectors and instance segmentors solely on ImageNet-1K. CutLER exhibits ...
IEEE

DIC: Deep Image Clustering for Unsupervised Image Segmentation

Abstract: Unsupervised segmentation is an essential pre-processing technique in many computer vision tasks. However, current unsupervised segmentation techniques are sensitive to the parameters such ...