Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

Rapid and accurate clinical diagnosis using mass spectrometry (MS) data is challenging due to the need for time-consuming preprocessing steps in conventional machine learning approaches. This study investigates the use of convolutional neural networks (CNNs) for MS data classification, focusing on transfer learning and a novel cumulative learning method to address the issue of limited training samples in medical applications. By training a single model on multiple small datasets from various biological contexts, the cumulative learning approach achieves over 98% classification accuracy for 1D clinical MS data, demonstrating its potential for improving the accuracy and speed of MS-based clinical diagnosis.

Khawla Seddiki, Philippe Saudemont, Frédéric Precioso, Nina Ogrinc, Maxence Wisztorski, Michel Salzet, Isabelle Fournier, Arnaud Droit