Current anomaly detection methods excel with benchmark industrial data but struggle with natural images and medical data due to varying definitions of 'normal' and 'abnormal.' This makes accurate identification of deviations in these fields particularly challenging. Especially for 3D brain MRI data, all the state-of-the-art models are reconstruction-based with 3D convolutional neural networks which are memory-intensive, time-consuming and producing noisy outputs that require further post-processing. We propose a framework called Simple Slice-based Network (SimpleSliceNet), which utilizes a model pre-trained on ImageNet and fine-tuned on a separate MRI dataset as a 2D slice feature extractor to reduce computational cost. We aggregate the extracted features to perform anomaly detection tasks on 3D brain MRI volumes. Our model integrates a conditional normalizing flow to calculate log likelihood of features and employs the Semi-Push-Pull Mechanism to enhance anomaly detection accuracy. The results indicate improved performance, showcasing our model's remarkable adaptability and effectiveness when addressing the challenges exists in brain MRI data. In addition, for the large-scale 3D brain volumes, our model SimpleSliceNet outperforms the state-of-the-art 2D and 3D models in terms of accuracy, memory usage and time consumption. Code is available at: https://anonymous.4open.science/r/SimpleSliceNet-8EA3.

A technician monitors a brain MRI scan ... [ ] session. Although Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological conditions that affects children and adults, it is still widely misunderstood. ADHD symptoms are commonly misdiagnosed or remain undiagnosed -- particularly among girls and women. In a new study, researchers made a breakthrough by potentially finding a far more robust mechanism through which ADHD diagnosis via brain MRI scans might become a reality in the future. A team of three researchers at the Yale School of Medicine delved into the data from MRI tests that were conducted on 7,805 children based in the United States.

Udunna C. Anazodo, PhD, is an assistant professor of neurology and neurosurgery at the Montreal Neurological Institute at McGill University. She is the founder and chair of the Consortium for Advancement of MRI Education and Research in Africa (CAMERA) and is currently leading efforts to create the Africa Neuroimaging Archive (AfNiA). Her research interests include diagnostic image analysis using artificial intelligence methods to enable quantitative PET and MRI for population neuroscience and global health. Maruf Adewole, MSc, is a medical physicist. He holds a bachelor's degree in physics and master's degree in medical physics from the Federal University of Technology Akure and University of Lagos, Nigeria, respectively.