AI Solutions in Healthcare – Medical Imaging

Mike McNamara

2020-03-16

Many cancers start with changes so small that no human can detect them, even with current medical imaging technology. However, AI programs can be trained with deep learning to see the very earliest changes in cell structure that typically develop into cancerous cells. Advances in medical imaging technologies, including 3D and 4D capabilities, real-time analytics, and GPU-accelerated processing, give radiologists powerful tools to make faster and more accurate diagnoses and recommendations for care. In addition, AI can also help prevent radiologist burnout.

The hippocampus is a major component of the human brain. It plays an important role in the consolidation of information from short-term memory to long-term memory, and in spatial memory that enables navigation. In Alzheimer's disease and other forms of dementia, the hippocampus is one of the first regions of the brain to suffer damage. Accurate identification of the hippocampus in magnetic resonance imaging (MRI) is an important step in the process of diagnosis, but this task of segmenting two neighboring small structures with high precision can be complex for radiologists and other doctors. Deep learning models can perform this work much faster and with higher accuracy, allowing medical practitioners to spend more time on patient diagnosis and care and less time examining images.

The data types in healthcare workloads vary—for example electronic health records, ultrasound, computed tomography (CT), MRI, and more. All this data contributes to different aspects of the healthcare services such as medical imaging, digital pathology, genomics, and other use cases. Model training requirements vary for distinct data types, and the goal is always to saturate the GPUs and provide the highest throughput at the lowest latency from the data storage.

In collaboration with NVIDIA, NetApp published a technical report titled “NetApp ONTAP AI Reference Architecture for Healthcare: Diagnostic Imaging.” The report offers directional guidance to healthcare providers to fast-track AI infrastructure initiatives specifically for diagnostic imaging practices in hospitals. It includes information about the high-level workflows used in developing deep learning (DL) data pipeline models for medical imaging, validation test cases and results, and sizing recommendations for deployments. The solution was validated with one NetApp® AFF A800, one NVIDIA DGX-2 system, and two Cisco Nexus 3232C 100Gb Ethernet (100GbE) switches.

NVIDIA Clara is a computational platform that enables developers to build, manage, and deploy intelligent medical imaging workflows. NVIDIA Clara Train SDK offers state-of-the art tools and technologies. This validation leverages Clara’s AI-assisted annotation to label a publicly available brain imaging dataset and train a hippocampus segmentation model based on ResNet-50 and AH-Net architectures.

For more information about NetApp AI solutions for healthcare, visit this page.

Mike McNamara

Mike McNamara 是 NetApp 產品和解決方案行銷的資深主管，在資料管理和雲端儲存行銷領域擁有超過 25 年的豐富經驗。在十年前加入 NetApp 之前，Mike 曾任職於 Adaptec、Dell EMC 和 HPE 等公司。Mike 是推出第一方雲端儲存產品和業界第一款雲端連線 AI/ML 解決方案 (NetApp)、統一化橫向擴充和混合雲儲存系統與軟體 (NetApp)、iSCSI 和 SAS 儲存系統與軟體 (Adaptec)，以及光纖通道儲存系統 (EMC CLARiiON) 的重要團隊領導者。此外他曾經擔任「光纖通道產業協會 (Fibre Channel Industry Association，FCIA)」的行銷主席，也是乙太網路技術高峰會議顧問委員會、乙太網路聯盟的成員，現在仍定期為業界期刊撰稿，並經常擔任活動講師。Mike 還透過 FriesenPress 出版了一本名為《橫向擴充儲存設備 - 企業資料管理的未來樣貌》的書籍，並被 Kapos 列為值得關注的 50 名 B2B 產品行銷人員。

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