Lymphoma

In-context learning enables multimodal large language models to classify cancer pathology images.

Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for parameter updates. Yet, in-context lear...

Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study.

INTRODUCTION: Axillary lymph node dissection (ALND) is the standard of care for breast cancer patients with positive sentinel lymph nodes (SLN), which are the first lymph nodes that drain the breast. However, many patients with positive SLNs may not have additional positive nodes, making the prediction of non-sentinel lymph node (NSLN) metastasis challenging. Reliable prognostic tools are essentia...

Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning.

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significant portion of lung cancer cases with distinct hist...

A deep learning model for prediction of autism status using whole-exome sequencing data.

Autism is a developmental disability. Research demonstrated that children with autism benefit from early diagnosis and early intervention. Genetic fac...

Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.

Non-small cell lung cancer (NSCLC) exhibits a comparatively slower rate of metastasis in contrast to small cell lung cancer, contributing to approxima...

Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.

PURPOSE: Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomark...

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool.

OBJECTIVES: The objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes...

A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.

BACKGROUND: Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured typ...

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computat...

Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant...

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heighten...

Identification of ferroptosis-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

Ferroptosis has emerged as a critical mechanism in the development and progression of various tumors, particularly diffuse large B-cell lymphoma (DLBC...

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive...

The SINFONIA project repository for AI-based algorithms and health data.

The SINFONIA project's main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental eff...

Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.

The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic los...

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders.

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders ...

AI-based automated construction of high-precision Geobacillus thermoglucosidasius enzyme constraint model.

Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. A...

Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.

Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histol...

Integrating color histogram analysis and convolutional neural networks for skin lesion classification.

The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with m...

Plant lncRNA-miRNA Interaction Prediction Based on Counterfactual Heterogeneous Graph Attention Network.

Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) provides a new perspective for understanding regulatory relatio...