Oncology/Hematology

Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and Convolutional Neural Network

Purpose: The scarcity of high-quality curated labeled medical training data remains one of the major limitations in applying artificial intelligence (AI) systems to breast cancer diagnosis. Deep models for mammogram analysis and mass (or micro-calcification) detection require training with a large volume of labeled images, which are often expensive and time-consuming to collect. To reduce this c...

Photon Absorption Remote Sensing Virtual Histopathology: Diagnostic Equivalence to Gold-Standard H&E Staining in Skin Cancer Excisional Biopsies

Photon Absorption Remote Sensing (PARS) enables label-free imaging of subcellular morphology by observing biomolecule specific absorption interactions. Coupled with deep-learning, PARS produces label-free virtual Hematoxylin and Eosin (H&E) stained images in unprocessed tissues. This study evaluates the diagnostic performance of these PARS-derived virtual H&E images in benign and malignant excis...

Towards a deep learning approach for classifying treatment response in glioblastomas

Glioblastomas are the most aggressive type of glioma, having a 5-year survival rate of 6.9%. Treatment typically involves surgery, followed by radio...

Towards a deep learning approach for classifying treatment response in glioblastomas

Glioblastomas are the most aggressive type of glioma, having a 5-year survival rate of 6.9%. Treatment typically involves surgery, followed by radio...

[Role of Artificial Intelligence in Improving Quality of Colonoscopy].

Colorectal cancer is a common malignancy and a major health concern in Korea. Although colonoscopy is an effective tool for screening and preventing c...

Quantum machine learning framework for longitudinal biomedical studies

Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, p...

Optimizing chemoradiotherapy for malignant gliomas: a validated mathematical approach

Malignant gliomas (MGs), particularly glioblastoma, are among the most aggressive brain tumors, with limited treatment options and a poor prognosis....

Enhancing Malignancy Detection and Tumor Classification in Pathology Reports: A Comparative Evaluation of Large Language Models.

BACKGROUND: Cancer registries require accurate and efficient documentation of malignancies, yet current manual methods are time-consuming and error-pr...

Development of a Synthetic Oncology Pathology Dataset for Large Language Model Evaluation in Medical Text Classification.

BACKGROUND: Large Language Models (LLMs) offer promising applications in oncology pathology report classification, improving efficiency, accuracy, and...

Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline

In this study, we built an end-to-end tumor-infiltrating lymphocytes (TILs) assessment pipeline within QuPath, demonstrating the potential of easily...

Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline

In this study, we built an end-to-end tumor-infiltrating lymphocytes (TILs) assessment pipeline within QuPath, demonstrating the potential of easily...

Radiometer Calibration using Machine Learning

Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of ...

Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations

Understanding the binding specificity between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to immunothe...

CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss

In ordinal classification, misclassifying neighboring ranks is common, yet the consequences of these errors are not the same. For example, misclassi...

Comprehensive Evaluation of Quantitative Measurements from Automated Deep Segmentations of PSMA PET/CT Images

This study performs a comprehensive evaluation of quantitative measurements as extracted from automated deep-learning-based segmentation methods, be...

Automatically Detecting Numerical Instability in Machine Learning Applications via Soft Assertions

Machine learning (ML) applications have become an integral part of our lives. ML applications extensively use floating-point computation and involve...

Optimizing Post-Cancer Treatment Prognosis: A Study of Machine Learning and Ensemble Techniques

The aim is to create a method for accurately estimating the duration of post-cancer treatment, particularly focused on chemotherapy, to optimize pat...

MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification

Lung cancer, a severe form of malignant tumor that originates in the tissues of the lungs, can be fatal if not detected in its early stages. It rank...

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction

AI-driven drug response prediction holds great promise for advancing personalized cancer treatment. However, the inherent heterogenity of cancer and...

ThyroidEffi 1.0: A Cost-Effective System for High-Performance Multi-Class Thyroid Carcinoma Classification

Background: Automated classification of thyroid Fine Needle Aspiration Biopsy (FNAB) images faces challenges in limited data, inter-observer variabi...