AI in cancer detection

Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning

This study uses a deep learning model to predict STK11 mutations in lung adenocarcinoma histopathology slides and finds that immune cell density is the key feature for classification.

Image model embeddings for digital pathology and drug development via self-supervised learning

This paper introduces DIME, a self-supervised learning pipeline for training image patch embeddings from whole slide images, enabling effective downstream tasks with limited data.

Deep learning trained on H&E tumor ROIs predicts HER2 status and Trastuzumab treatment response in HER2+ breast cancer

This paper presents a CNN that predicts HER2 status and trastuzumab response from H&E images with higher accuracy than IHC.

Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models

This paper introduces a deep learning model that predicts endometrial cancer subtypes and mutations from histopathology images, offering a faster alternative to genomic sequencing.

Deep Learning Features Encode Interpretable Morphologies within Histological Images

This paper introduces 'mones' as interpretable CNN features from H&E images that correlate with biological phenotypes and can accurately classify tumors and normals.

Benchmarking artificial intelligence methods for end-to-end computational pathology

This study benchmarks AI methods for end-to-end computational pathology, showing ResNet and ViT outperform MIL and CLAM in mutation prediction but not in tumor subtyping.

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

This paper shows that digital histopathology batch effects in TCGA data affect DL model accuracy and bias, and proposes a method to address this issue.

A deep learning approach for tissue spatial quantification and genomic correlations of histopathological images

The paper develops a CNN model to classify epithelial and stromal tissues in histopathological images and correlates these with gene expression data to identify biological processes.

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

The paper introduces a deep learning tool to classify xenograft cells in histological images by analyzing contextual features, achieving up to 96% accuracy in distinguishing human and murine cells.

Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification

The paper introduces DeepLIIF, a deep learning framework that translates IHC images to multiplex IF images and quantifies single-cell IHC markers.

Spatial analysis of tumor infiltrating lymphocytes based on deep learning using histopathology image to predict progression-free survival in colorectal cancer

This paper uses deep learning to analyze histopathology images of colorectal cancer to predict progression-free survival by quantifying tumor infiltrating lymphocytes.

Using Histopathology Images to Predict Chromosomal Instability in Breast Cancer: A Deep Learning Approach

This study uses deep learning to predict chromosomal instability (CIN) in breast cancer from histopathology images, achieving an AUC of 0.822 in a test set.

End-to-End Deep Learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images

This paper develops and evaluates deep learning models to detect metastatic breast cancer in axillary lymph nodes using digital pathology images, finding DenseNet121 achieves the highest performance.

piNET: An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

piNET is a deep learning framework for Ki67 proliferation index calculation in breast cancer images, achieving high accuracy and correlation across diverse datasets.

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images

This paper uses CNNs to classify and compare histopathological images across cancer types, revealing conserved spatial patterns and mutation-related features.

VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts

The paper introduces a deep learning tool for automated histologic feature quantification in murine pancreatic disease models, showing high correlation with immunostaining results and accurate differentiation of disease states.

Convolutional neural network for automated mass segmentation in mammography

The paper develops a Vanilla U-Net model for mass segmentation in mammography, achieving high accuracy and IOU metrics through data augmentation and transfer learning.

A pilot exploratory study of the potentials of deep learning methods in cancer image segmentation and classification

The paper does not provide substantive content as the authors have withdrawn the manuscript due to data-sharing issues.

Spatial Transcriptomics Inferred from Pathology Whole-Slide Images Links Tumor Heterogeneity to Survival in Breast and Lung Cancer

The paper develops deep learning methods to infer spatial gene expression from pathology whole-slide images and links tumor heterogeneity to survival in breast and lung cancer.

Transfer learning from simulations improves the classification of OCT images of glandular epithelia

This study shows that transfer learning from synthetic OCT image simulations improves classification of esophageal tissue in BE patients compared to models trained on clinical data with uncertain labels.

PyHIST: A Histological Image Segmentation Tool

PyHIST is a tool for segmenting and preprocessing histological images to prepare them for machine learning applications.

SHIFT: speedy histological-to-immunofluorescent translation of whole slide images enabled by deep learning

This paper introduces a deep learning method called SHIFT that translates histological images into virtual immunofluorescence images, improving efficiency and reducing costs in cancer diagnostics.

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer

This paper develops a method to predict molecular phenotypes from cancer pathology images using interpretable features, showing correlations with tumor microenvironment markers and predictive power for molecular signatures.

Pan-cancer image-based detection of clinically actionable genetic alterations

This paper develops a deep learning method to predict genetic alterations from histological images across multiple cancer types.

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

This paper uses deep transfer learning on H&E images to correlate histopathological patterns with genomic and survival data across 28 cancer types, revealing associations between histopathology and molecular features.

Predicting tumour mutational burden from histopathological images using multiscale deep learning

This paper develops a deep learning model to predict tumour mutational burden from histopathological images, achieving high accuracy comparable to genetic sequencing.

Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks

This study uses pre-trained CNNs to extract features from HCC histopathological images, showing their ability to predict survival outcomes and correlate with biological pathways.

Synthesis of diagnostic quality cancer pathology images

This study uses GANs to synthesize high-resolution cancer pathology images, showing they are visually indistinguishable from real images and perform as well as real images in diagnostic tasks.

Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public dataset

The paper uses deep convolutional neural networks on pathological images from the TCGA-BRCA dataset to predict breast cancer molecular subtypes with 78% validation accuracy.

Computational staining of pathology images to study tumor microenvironment in lung cancer

The paper develops a deep learning tool to analyze pathology images and identify TME features predictive of survival in lung cancer.