Oncology/Hematology

Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis

Large language models (LLMs) have demonstrated immense capabilities in understanding textual data and are increasingly being adopted to help researchers accelerate scientific discovery through knowledge extraction (information retrieval), knowledge distillation (summarizing key findings and methodologies into concise forms), and knowledge synthesis (aggregating information from multiple scientif...

[Artificial intelligence for lymph node metastasis prediction in gastric cancer: research progress].

Gastric cancer is a common tumor in China, and lymph node metastasis (LNM) is an independent prognostic factor for it. Accurately determining the risk of LNM in gastric cancer can help to formulate the treatment plan and estimate its staging and prognosis preoperatively. Existing means for evaluating LNM in gastric cancer have shortcomings such as low accuracy and high subjectivity. In recent year...

Registration of Longitudinal Liver Examinations for Tumor Progress Assessment

Assessing cancer progression in liver CT scans is a clinical challenge, requiring a comparison of scans at different times for the same patient. Pra...

ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer

The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL...

Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning

A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells ...

Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images

Cancer diagnosis and prognosis primarily depend on clinical parameters such as age and tumor grade, and are increasingly complemented by molecular d...

Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation

Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT ...

Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths

Background: Multiparametric breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and deline...

Synthetic CT image generation from CBCT: A Systematic Review

The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in ...

MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer Analysis

Computed tomography (CT) and clinical numeric data are essential modalities for cancer evaluation, but building large-scale multimodal training data...

Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET

Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current in...

Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops

Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prost...

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images

Blood vessels (BVs) play a critical role in the Tumor Micro-Environment (TME), potentially influencing cancer progression and treatment response. Ho...

Early Detection and Classification of Breast Cancer Using Deep Learning Techniques

Breast cancer is one of the deadliest cancers causing about massive number of patients to die annually all over the world according to the WHO. It i...

Can open source large language models be used for tumor documentation in Germany? -- An evaluation on urological doctors' notes

Tumor documentation in Germany is largely done manually, requiring reading patient records and entering data into structured databases. Large langua...

Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with lung metastases being the most common site of distant spread and...

Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data

Given multi-type point maps from different place-types (e.g., tumor regions), our objective is to develop a classifier trained on the source place-t...

A baseline for machine-learning-based hepatocellular carcinoma diagnosis using multi-modal clinical data

The objective of this paper is to provide a baseline for performing multi-modal data classification on a novel open multimodal dataset of hepatocell...

Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases

Establishing the reproducibility of radiomic signatures is a critical step in the path to clinical adoption of quantitative imaging biomarkers; howe...

Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning

Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this stud...