At Rise of AI 2019 in Berlin, Florin Manaila, Senior AI Architect and Inventor at IBM Systems Hardware Europe, presents an IBM proof of technology for detecting breast cancer from MRI and histopathological images using convolutional neural networks. He explains that diagnosis today moves through clinical evaluation, mammography, sometimes ultrasound, MRI and biopsy, with lab analysis of tissue samples alone taking two to three weeks, and that early detection is linked to much higher survival, citing National Cancer Institute UK figures. He frames the work within the regulatory category of software as a medical device and describes an infrastructure stack covering data governance, a data lake, distributed training and bias detection. Using IBM's AC922 hardware and PowerAI Vision software running on-premise in hospitals, his team trained a classification model on more than 10,000 histopathology images, reaching 93 percent accuracy after image augmentation. He describes next steps including extending the approach to mammography, combining multiple imaging modalities with patient context, and building AI operations for monitoring deployed models, while stressing that hospitals train their own models on their own verified, labeled data.
Key takeaways
- Manaila says histopathology lab analysis alone takes two to three weeks and is the most time-consuming step in breast cancer diagnosis.
- The team's model reached 93 percent accuracy on histopathology images after augmenting the training data by flipping and rotating images.
- He frames the algorithm under FDA/IMDRF rules as software as a medical device, requiring ongoing monitoring and logging after deployment.
- The system runs on-premise in hospitals on IBM AC922 hardware with PowerAI Vision, since hospitals are reluctant to send data to the cloud.
- Mammography detection is harder than histopathology because images are grayscale, reaching about 91 percent accuracy so far.
Detailed notes
The diagnostic bottleneck
Manaila describes the three stages of breast cancer diagnosis, noting that stage two, covering clinical evaluation, diagnosis and staging, typically takes four to six weeks. Patients move from clinical evaluation to mammography, sometimes an ultrasound, then an MRI for a 3D view, and finally a biopsy whose lab analysis alone takes two to three weeks and determines tumor type, grade and receptor status such as HER2. He cites National Cancer Institute UK five-year survival statistics showing that early detection is linked to survival rates around 90 to 98 percent, compared with much lower rates for advanced stage three or four cancer, and identifies the histopathology lab step as the most time-consuming part of the pipeline, asking whether deep learning can accelerate it.
Regulation and the infrastructure stack
Because a trained algorithm that recognizes cancer in an image falls under FDA and IMDRF definitions of software as a medical device, Manaila stresses that building the model is only part of the work: the product life cycle also requires continuous monitoring, tracking and logging of what the algorithm produces. He outlines an infrastructure stack starting from healthcare regulations and data governance, moving through a data lake for storing images, distributed computing for training, and machine learning algorithms, and ending with explainable AI tools to detect and remove bias in the data sets, using IBM's AI Fairness 360 toolkit.
Building the imaging pipeline
The pipeline captures images and DICOM metadata such as age and sex from mammography, MRI and biopsy/histopathology, pre-processes formats specific to each modality, and passes them to doctors and specialists for labeling into data sets for each imaging type. Trained neural networks then classify, detect or segment tumors, localize and size them, and combine the prediction with patient context to produce a description for the doctor, with intended deployment either on a server in a hospital data center or on a small embedded medical device.
Hardware, software and the histopathology experiment
The system runs on IBM's AC922 deep learning hardware for higher throughput and accuracy, paired with PowerAI Vision software that gives doctors a web interface for labeling, training and inference without managing the underlying deep learning infrastructure; it runs on-premise because hospitals are reluctant to send data to the cloud. Using a data set of more than 10,000 histopathology microscopy images, the team trained a GoogLeNet classification model with class activation mapping at 40x magnification, reaching an initial 87 percent accuracy that improved to 93 percent after augmenting the training images through flipping and rotation, with inference under one second and a heat map showing which image regions drove the classification.
Next steps and Q&A highlights
Manaila describes extending the approach to mammography, which is harder because images are grayscale and currently reaches about 91 percent accuracy, and to combining mammography, MRI and histopathology predictions with patient age and sex for a better overall score, plus a platform for cases needing input from multiple specialists, such as metastasis to the lungs. He also mentions moving inference to embedded GPUs and FPGAs and building AI operations to monitor deployed models. Asked whether the work extends to metabolic diseases such as liver fibrosis or cardiovascular conditions, he says not yet. Asked why real-time feedback matters for a critical diagnosis, he says it gives specialists an immediate signal, for example a percentage likelihood of a finding at a given magnification, so they know when to look closer, which is especially valuable where lab queues are long. Asked how a doctor uses the system in practice, he explains two paths: direct API integration with the hospital's existing imaging system for automatic predictions, or exporting and uploading an image through the web interface to get a prediction.
Quotes
Inference in under one second to give you 90 percent accuracy is close to the human error, which is five percent.
Florin Manaila
Questions answered in this talk
How accurate is deep learning at detecting breast cancer from histopathology images?
According to Florin Manaila at Rise of AI 2019, IBM's experiment using PowerAI Vision on more than 10,000 histopathology images reached 93 percent accuracy after applying image augmentation such as flipping and rotating, up from an initial 87 percent.
Why is early detection so important for breast cancer survival?
According to Florin Manaila at Rise of AI 2019, citing National Cancer Institute UK statistics, early-stage detection is linked to survival rates as high as 90 to 98 percent, compared with much lower rates for advanced stage three or four cancer.
How does a hospital doctor use an AI system like this in practice?
According to Florin Manaila at Rise of AI 2019, the system runs on-premise in the hospital, and doctors either integrate it via API with their existing imaging system for automatic predictions or export and upload images through a web interface to receive a prediction.