Titlebook: Data Engineering in Medical Imaging; Second MICCAI Worksh Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2025 The Editor

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,Evaluating Histopathology Foundation Models for?Few-Shot Tissue Clustering: An?Application to?LC250, we create a clean version of LC25000. We then evaluate the quality of features extracted by these foundational models, using the clustering task as a benchmark. Our contributions are: 1) We publicly release our semi-automatic annotation pipeline along with the LC25000-clean dataset to facilitate a

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,Synthetic Simplicity: Unveiling Bias in?Medical Data Augmentation,irst demonstrate this vulnerability on a digit classification task, where the model spuriously utilizes the source of data instead of the digit to provide an inference. We provide further evidence of this phenomenon in a medical imaging problem related to cardiac view classification in echocardiogra

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,Pre-processing and?Quality Control of?Large Clinical CT Head Datasets for?Intracranial Arterial Cals (structural similarity index measure) to triage assessment of individual image series. Additionally, we propose superimposing thresholded binary masks of the series to inspect large quantities of data in parallel. We identify and exclude unrecoverable samples and registration failures..In total, o

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,Simple is More: Efficient Liver View Classification in?Ultrasound Images Using Minimal Labeled DataimpleClassifier achieved an accuracy of 91.5% with 257 labeled frames, while ResNet-18 and MLP-Mixer required 801 labeled frames each to achieve 70.2% and 86% accuracy, respectively. These findings demonstrate that combining active learning with an AI classifier, regardless of its complexity, can im