Muhammad Febrian Rachmadi

This paper predicts one-year WMH evolution post-stroke using MRI and stroke lesion data with a probabilistic deep learning model, achieving improved accuracy through joint segmentation and a new loss function.

This study maps prefrontal cortex circuits in marmosets, revealing distinct local and long-distance projection patterns and their implications for primate brain organization.

The paper introduces MiniVess, a rodent cerebrovasculature dataset for testing machine learning tools in biomedical image analysis.

The paper introduces the Brain/MINDS Marmoset Connectivity Atlas, providing a resource for exploring neuronal tracer data and tractography in a common stereotaxic space.

This paper introduces DEP models to predict white matter hyperintensity evolution in brain MRI, showing that semi-supervised models with Gaussian noise inputs perform best.

The paper introduces LOTS-IM, an unsupervised method for detecting white matter hyperintensities and MS lesions in MRI, showing better performance than existing methods.

This paper introduces a Dilated Saliency U-Net for WMH segmentation using an irregularity age map, achieving better performance with fewer parameters and shorter training time.

The paper evaluates enhanced machine learning techniques for segmenting ischaemic stroke lesions in MR perfusion images using a CNN, finding data augmentation with binary closing improves segmentation accuracy.

This paper introduces DEP-GAN, a GAN model that predicts WMH evolution in small vessel disease using irregularity maps and two discriminators, achieving 72.01% accuracy in predicting WMH evolution.

This study uses an Irregularity Age Map to adapt transfer learning for brain lesion segmentation and predict WMH progression/regression without manual labels.