Mixing Histopathology Prototypes into Robust Slide-Level Representations
  for Cancer Subtyping
Joshua Butke Noriaki Hashimoto Ichiro Takeuchi Hiroaki Miyoshi Koichi Ohshima Jun Sakuma
Whole-slide image analysis via the means of computational pathology often
relies on processing tessellated gigapixel images with only slide-level labels
available. Applying multiple instance learning-based methods or transformer
models is computationally expensive as, for each image, all instances have to
be processed simultaneously. The MLP-Mixer is an under-explored alternative
model to common vision transformers, especially for large-scale datasets. Due
to the lack of a self-attention mechanism, they have linear computational
complexity to the number of input patches but achieve comparable performance on
natural image datasets. We propose a combination of feature embedding and
clustering to preprocess the full whole-slide image into a reduced prototype
representation which can then serve as input to a suitable MLP-Mixer
architecture. Our experiments on two public benchmarks and one inhouse
malignant lymphoma dataset show comparable performance to current
state-of-the-art methods, while achieving lower training costs in terms of
computational time and memory load. Code is publicly available at
https://github.com/butkej/ProtoMixer.