Vision Transformers Based AI Models For Predicting Colorectal Cancer from Digital Pathology WSI: Use Case Of MHIST dataset
Kondejkar, T.; Tunik, G.; Amal, S.
Show abstract
This study investigates the efficacy of transformer-based deep learning architectures--specifically, Vision Transformer (ViT), Class Attention in Image Transformers (CaiT), and Data-Efficient Image Transformers (DeiT)--for the binary classification of colorectal polyps using the Minimalist Histopathology Image Analysis Dataset (MHIST). The dataset comprises 3,152 hematoxylin and eosin (H&E)-stained Formalin Fixed Paraffin-Embedded (FFPE) images annotated as either Hyperplastic Polyps (HP) or Sessile Serrated Adenomas (SSA). A rigorous evaluation was conducted using a 5-fold stratified cross-validation methodology, and performance was quantified using metrics including accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results revealed that transformer architectures, particularly CaiT (accuracy of 90.18%, AUC-ROC of 95.52%), outperformed traditional convolutional neural networks (CNNs). The superior performance of CaiT is attributed to its specialized class-attention mechanisms, effectively capturing nuanced morphological differences essential for accurate histopathological classification. These findings underscore the potential of transformer-based models to enhance diagnostic precision, reduce variability in pathological assessment, and facilitate earlier and more reliable colorectal cancer screening.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging 94%
- Deep learning models for poorly differentiated colorectal adenocarcinoma classification in whole slide images using transfer learning 94%
- An Explainable Web-Based Diagnostic System for Alzheimer's Disease Using XRAI and Deep Learning on Brain MRI 93%
Similar papers in this journal
Similar papers in this journal
- On evaluation metrics for medical applications of artificial intelligence 96%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 95%
- Aggregation of Cohorts for Histopathological Diagnosis with Deep Morphological Analysis 95%
Similar papers in this journal
- Using an Anomaly Detection Approach for the Segmentation of Colorectal Cancer Tumors in Whole Slide Images 96%
- Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope 95%
- Improving generalization of deep learning models for diagnostic pathology by increasing variability in training data: experiments on osteosarcoma subtypes 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.