  While model architectures and training strategies have become more generic
and flexible with respect to different data modalities over the past years, a
persistent limitation lies in the assumption of fixed quantities and
arrangements of input features. This limitation becomes particularly relevant
in scenarios where the attributes captured during data acquisition vary across
different samples. In this work, we aim at effectively leveraging data with
varying features, without the need to constrain the input space to the
intersection of potential feature sets or to expand it to their union. We
propose a novel architecture that can directly process data without the
necessity of aligned feature modalities by learning a general embedding space
that captures the relationship between features across data samples with
varying sets of features. This is achieved via a set-transformer architecture
augmented by feature-encoder layers, thereby enabling the learning of a shared
latent feature space from data originating from heterogeneous feature spaces.
The advantages of the model are demonstrated for automatic cancer cell
detection in acute myeloid leukemia in flow cytometry data, where the features
measured during acquisition often vary between samples. Our proposed
architecture's capacity to operate seamlessly across incongruent feature spaces
is particularly relevant in this context, where data scarcity arises from the
low prevalence of the disease. The code is available for research purposes at
https://github.com/lisaweijler/FATE.
