  In digital pathology tasks, transformers have achieved state-of-the-art
results, surpassing convolutional neural networks (CNNs). However, transformers
are usually complex and resource intensive. In this study, we developed a novel
and efficient digital pathology classifier called DPSeq, to predict cancer
biomarkers through fine-tuning a sequencer architecture integrating horizon and
vertical bidirectional long short-term memory (BiLSTM) networks. Using
hematoxylin and eosin (H&E)-stained histopathological images of colorectal
cancer (CRC) from two international datasets: The Cancer Genome Atlas (TCGA)
and Molecular and Cellular Oncology (MCO), the predictive performance of DPSeq
was evaluated in series of experiments. DPSeq demonstrated exceptional
performance for predicting key biomarkers in CRC (MSI status, Hypermutation,
CIMP status, BRAF mutation, TP53 mutation and chromosomal instability [CING]),
outperforming most published state-of-the-art classifiers in a within-cohort
internal validation and a cross-cohort external validation. Additionally, under
the same experimental conditions using the same set of training and testing
datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and
EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest
AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP
status. Furthermore, DPSeq required less time for both training and prediction
due to its simple architecture. Therefore, DPSeq appears to be the preferred
choice over transformer and CNN models for predicting cancer biomarkers.
