AI in cancer detection

SmartHisto: Bayesian Active Learning for Histology Images

SmartHisto reduces annotation effort for histopathology images using Bayesian active learning, achieving higher mean IoU than competing methods.

DeepPathway: Predicting Pathway Expression from Histopathology Images

DeepPathway predicts pathway expression from H&E images using contrastive learning, validated on prostate cancer and brain tumor datasets.

GlomExtractor: a versatile tool for extracting glomerular patches from whole slide kidney biopsy images

This paper introduces GlomExtractor, a tool for extracting glomerular patches from kidney biopsy images, and shows how processing strategies affect machine learning performance.

Post-operative tissue fragment puzzling using histopathological vision transformer alignment HiViTAlign

This paper introduces HiViTAlign, a computational pipeline that reassembles fragmented histopathological tissue sections using a vision transformer model, enabling spatial reconstruction of tissue fragments without ground truth.

CenSegNet: a generalist high-throughput deep learning framework for centrosome phenotyping at spatial and single-cell resolution in heterogeneous tissues

CenSegNet quantifies centrosome abnormalities in breast cancer tissues at single-cell resolution, revealing subtype-specific associations with clinical and genomic features.

Automatic Bevacizumab Response Prediction in Ovarian Cancer from Digital Pathology Images via Novel AI-based Computational Pipeline

The paper presents an AI-based pipeline for predicting bevacizumab response in ovarian cancer using digital pathology images, achieving improved AUC performance compared to existing models.

Features fusion or not: harnessing multiple pathological foundation models using Meta-Encoder for downstream tasks fine-tuning

The paper introduces Meta-Encoder, a framework that combines multiple pathological foundation models to improve downstream task performance in computational pathology.

ADAPTIVE MULTI-SCALE GRAPH TRANSFORMER FRAMEWORK FORHISTOPATHOLOGICAL IMAGES

The paper introduces an Adaptive Multi-Scale Graph Transformer (AMGT) for histopathological image classification, achieving better performance and efficiency than existing models.

Exploring Multi-Scale Local and Global Features in Whole Slide Images Using State Space Models

This paper introduces MambaWSI, a state space model for whole slide image classification that extracts multi-scale local and global features, achieving better performance than existing methods.

Artificial Intelligence-enabled Histological Analysis in Preclinical Respiratory Disease Models: A Scoping Review

This paper reviews AI applications in histological analysis of preclinical respiratory disease models, finding high performance but gaps in validation, transparency, and standardization.

ViFIT-assisted Histopathology: From H&E Style Standardization to Virtual Fiber Image Transformation

ViFIT-assisted histopathology converts diverse H&E-stained images into standardized H&E images and generates virtual fiber images, improving diagnostic workflows across cancer types.

LazySlide: accessible and interoperable whole slide image analysis

LazySlide provides an open-source Python package for whole-slide image analysis and multimodal integration, enabling efficient histopathology workflows with minimal setup.

Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis

The paper introduces a subphase-labeled mitotic dataset to improve mitosis detection and subtyping, enhancing domain generalization and enabling biological insights into mitotic subphases.

Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological images

This paper shows that deep learning models for predicting biomarkers from histology images are confounded by interdependencies among biomarkers, leading to less accurate and less reliable predictions.

Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in H&E-Stained Digital Pathology Images

This study evaluates pathology foundation models for detecting tertiary lymphoid structures in cancer tissues, finding strong performance in PDAC but challenges in HNSCC due to tumor heterogeneity.

Impact of tissue staining and scanner variation on the performance of pathology foundation models: a study of sarcomas and their mimics

Across 14 sarcoma and mimic subtypes, six pathology foundation models beat ResNet50 but lost accuracy under multi-institutional staining; adding 121 externally stained slides narrowed the gap.

Detection of prostate cancer in 3D pathology datasets via generative immunolabeling

This paper develops a 3D computational pipeline called SIGHT to detect prostate cancer in pathology datasets using generative immunolabeling, achieving an F1 score comparable to pathologist agreement.

Normal Breast Tissue (NBT)-Classifiers: Advancing Compartment Classification in Normal Breast Histology

CNN patch classifiers trained only on normal breast H&E slides label epithelium, stroma and adipocytes with AUCs of 0.98–1.00 on three external cohorts and feed a lobule-detection pipeline.

StainStyleSampler: Clustering-based sampling of whole slide image appearances

StainStyleSampler clusters and samples stain styles from whole slide images to improve machine learning robustness across different staining appearances.

Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning

This paper develops a deep learning model to predict molecular alterations in never-smoker lung adenocarcinomas from histological images, achieving high accuracy for most features.

LIMPACAT : Multi-Omics Attention Transformer for Immune Prediction in Liver Cancer Using Whole-Slide Imaging

LIMPACAT uses whole-slide images and multi-omics data to predict immune cell levels in liver cancer, achieving 80% accuracy in prognosis classification.

HistoSB-Net: Semantic Bridging for Data-Limited Cross-Modal Histopathological Diagnosis

HistoSB-Net adapts pre-trained vision-language models for histopathological diagnosis with limited data, improving performance through semantic bridging.

Mitosis detection in the wild using detection transformers

This paper develops a deep learning model for mitotic figure detection that generalizes across diverse histological regions and tumor types, achieving an F1-score of 0.7769.

FourierMIL: Fourier filtering-based multiple instance learning for whole slide image analysis

FourierMIL uses Fourier filtering and multiple instance learning to analyze whole-slide images, outperforming existing methods in tumor classification.

Label Noise in Pathological Segmentation is Overlooked, Leading to Potential Overestimation of Artificial Intelligence Models

This study investigates how label noise in pathology segmentation affects AI model performance, finding that boundary-altering noise leads to overestimation of model accuracy.

Diffusion Models vs. DCGANs for Class-Imbalanced Lung Cancer CT Classification: A Comparative Study

This study compares diffusion models and DCGANs for class-imbalanced lung cancer CT classification, finding diffusion models achieve higher accuracy and better recall for benign cases.

Predicting microsatellite instability from whole slide images using texture features

This study predicts microsatellite instability from whole slide images using texture features, achieving high AUC scores on both tile and sample levels.

Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age

This paper develops models linking histological images to genotype, transcriptome, and age, identifying 906 imageQTLs and predicting gene expression and chronological age from images.

Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer

This paper develops CPMP, a weakly supervised transformer model, to predict MammaPrint recurrence risk from breast cancer histopathological slides, achieving an AUROC of 0.824.

Path2Omics: Enhanced transcriptomic and methylation prediction accuracy from tumor histopathology

Path2Omics predicts gene expression and methylation from histopathology slides across 23 cancer types, outperforming previous models and showing effectiveness in predicting patient outcomes.