Posts by Prateek Chokse
AMD Life Science 26.08: A Complete Medical Imaging Stack on AMD Instinct™ GPUs
- 12 November 2026
Deep learning and medical imaging are reshaping healthcare, from tumor detection in whole-slide pathology to whole-body segmentation in computed tomography (CT). But a medical imaging workflow isn’t a single step. A slide or a scan has to be read off disk and decoded, fed through a model, and often handed to a clinician to review and refine. To serve this work well, AMD Instinct™ has to accelerate every step, not just one of them.
MONAILabel 0.8.5 on AMD Instinct™ GPUs: Interactive Medical-Image Annotation
- 09 November 2026
Behind every well-trained medical imaging model is a painstakingly labeled dataset, and behind that dataset is a human expert drawing contours one slice at a time. MONAILabel exists to make that human faster. Developed by Project MONAI (Medical Open Network for AI), it is a widely used open-source active-learning annotation server for medical imaging. It bridges 3D Slicer, the Open Health Imaging Foundation (OHIF) Viewer, and QuPath to MONAI-powered deep learning models, so radiologists and researchers can label 3D computed tomography (CT) and magnetic resonance imaging (MRI) volumes and whole-slide images interactively, with the model suggesting and the human refining.
Bringing the MONAI Model Zoo to AMD Instinct™ GPUs: Validated Inference on ROCm
- 30 October 2026
The Medical Open Network for AI (MONAI) Model Zoo is often the first stop when a researcher is building a medical imaging application: a shared shelf of ready-to-use, peer-reviewed models, where you can find a whole-body computed tomography (CT) segmenter or an interactive annotation network already trained, packaged, and ready to run.
Accelerating MONAI Digital-Pathology Pipelines on AMD Instinct™ GPUs with hipCIM
- 26 October 2026
A digital-pathology model is only as fast as the pipeline that feeds it. That’s simple to overlook when the conversation turns to network architectures and FLOPs, but on real whole-slide workloads the truth is stubborn: a great deal of the wall-clock time goes to reading gigapixel slides off disk and decoding them into the small tiles the model consumes. Speed up the model and leave the reader untouched, and the pipeline barely moves.
hipCIM 26.06: GPU-Accelerated Whole-Slide Image I/O on AMD Instinct™ GPUs
- 20 October 2026
Every whole-slide image tells a story in pixels. A whole-slide image is a glass pathology slide scanned into a single very large digital image, stored as a pyramid of small tiles so a viewer can pan and zoom it. A single biopsy digitized at 20× magnification can span tens of thousands of pixels on each side and weigh in at several gigabytes. Before a model can learn anything from that slide, something has to read it off disk, decode its thousands of small JPEG-compressed tiles, and turn them into tensors the accelerator can work on. In digital pathology, that unglamorous first step is often where the wall-clock time quietly disappears.