AI tool spots blood cell abnormalities missed by doctors

Good news! This seems to be an excellent example of applying machine learning & AI to medicine.

"Researchers have created a system called CytoDiffusion that uses generative AI – the same type of technology behind image generators such as DALL-E – to study the shape and structure of blood cells.

Unlike many AI models, which are trained to simply recognise patterns, CytoDiffusion ... could accurately identify a wide range of normal blood cell appearances and spot unusual or rare cells that may indicate disease.

Spotting subtle differences in blood cell size, shape and appearance is a cornerstone of diagnosing many blood disorders. But the task requires years of training, and even then, different doctors can disagree on difficult cases. ..."

From the abstract:

"Blood cell morphology assessment via light microscopy constitutes a cornerstone of haematological diagnostics, providing crucial insights into diverse pathological conditions. This complex task demands expert interpretation owing to subtle morphological variations, biological heterogeneity and technical imaging factors that obstruct automated approaches.

Conventional machine learning methods using discriminative models struggle with domain shifts, intraclass variability and rare morphological variants, constraining their clinical utility.

We introduce CytoDiffusion, a diffusion-based generative classifier that faithfully models the distribution of blood cell morphology, combining accurate classification with robust anomaly detection, resistance to distributional shifts, interpretability, data efficiency and uncertainty quantification that surpasses clinical experts.

Our approach outperforms state-of-the-art discriminative models in

anomaly detection (area under the curve, 0.990 versus 0.916),

resistance to domain shifts (0.854 versus 0.738 accuracy) and

performance in low-data regimes (0.962 versus 0.924 balanced accuracy).

In particular, CytoDiffusion generates synthetic blood cell images that expert haematologists cannot distinguish from real ones (accuracy, 0.523; 95% confidence interval: [0.505, 0.542]), demonstrating good command of the underlying distribution.

Furthermore, we enhance model explainability through directly interpretable counterfactual heat maps.

Our comprehensive evaluation framework establishes a multidimensional benchmark for medical image analysis in haematology, ultimately enabling improved diagnostic accuracy in clinical settings."

AI tool spots blood cell abnormalities missed by doctors | University of Cambridge "An AI tool that can analyse abnormalities in the shape and form of blood cells, and with greater accuracy and reliability than human experts, could change the way conditions such as leukaemia are diagnosed."

Deep generative classification of blood cell morphology (open access)

Fig. 1: Overview of the diffusion-based classification model.

Fig. 4: Counterfactual visualizations for model explainability.