Lymphoma

Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans

Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for improving patient outcomes. Here, we compare the performance of two machine learning models - FMCIB+XGBoost, a supervised model with domain-specific pretraining, and Dinov2+ABMIL, a self-supervised model with attention-based multiple-instance learning...

Personalized Tree based progressive regression model for watch-time prediction in short video recommendation

In online video platforms, accurate watch time prediction has become a fundamental and challenging problem in video recommendation. Previous research has revealed that the accuracy of watch time prediction highly depends on both the transformation of watch-time labels and the decomposition of the estimation process. TPM (Tree based Progressive Regression Model) achieves State-of-the-Art performa...

Predicting the risk of ibrutinib in combination with R-ICE in patients with relapsed or refractory DLBCL using explainable machine learning algorithms.

Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) poses significant therapeutic challenges due to heterogeneous patient outcomes. This stud...

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems

We present eACGM, a full-stack AI/ML system monitoring framework based on eBPF. eACGM collects real-time performance data from key hardware componen...

Pixels to Prognosis: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures Across Multicentre NSCLC Data

Purpose: To evaluate the impact of harmonization and multi-region CT image feature integration on survival prediction in non-small cell lung cancer ...

Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI

Purpose: Magnetic Resonance Imaging (MRI) enables non-invasive assessment of brain abnormalities during early life development. Permanent magnet sca...

Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.

BACKGROUND: Esophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can i...

Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties

We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most e...

Improving the discovery of near-Earth objects with machine-learning methods

We present a comprehensive analysis of the digest2 parameters for candidates of the Near-Earth Object Confirmation Page (NEOCP) that were reported b...

An integrated deep learning model for early and multi-class diagnosis of Alzheimer's disease from MRI scans.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, behavior, and cognitive function. Early and accurat...

Pretrained hybrid transformer for generalizable cardiac substructures segmentation from contrast and non-contrast CTs in lung and breast cancers

AI automated segmentations for radiation treatment planning (RTP) can deteriorate when applied in clinical cases with different characteristics than...

An AI-driven strategy for active compounds discovery and non-destructive quality control in traditional Chinese medicine: A case of Xuefu Zhuyu Oral Liquid.

The modernization and globalization of traditional Chinese medicine (TCM) face challenges such as unclear active compounds and inadequate quality cont...

Using machine learning methods to predict the diabatic bond dissociation energy of non-heme iron complexes.

Bond dissociation energy (BDE) is an important property in chemical research. In the process of non-heme iron complex catalytic reactions, diabatic BD...

[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].

Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision a...

Learning-Based Classification of B- and T-Cell Lymphoma on Histopathological Images: A Multicenter Study.

Lymphoma, a clonal malignancy from lymphocytes, includes diverse subtypes requiring distinct immunohistochemical stains for accurate diagnosis. Limite...

Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques.

: Partial nephrectomy (PN) is the preferred option for treating localized cT1 renal cell carcinoma (RCC), as it preserves renal function in most patie...

Predicting growth parameters of biofertilizer inoculated pepper, using root capacitance assessments and artificial neural networks in two soils.

Monitoring the root system plays an important role in understanding plant physiological processes; however, its assessment using non-destructive metho...

Blood-brain barrier crossing biopolymer targeting c-Myc and anti-PD-1 activate primary brain lymphoma immunity: Artificial intelligence analysis.

Primary Central Nervous System Lymphoma is an aggressive central nervous system neoplasm with poor response to pharmacological treatment, partially du...

Identification of Phase Transformations in Alloy and Non-Alloy Steel During Austempering Using Acoustic Emission and Neural Network.

This research was carried out for selected alloy (bearing) and non-alloy (tool) steel. The steels were subjected to austempering. The hardening temper...

Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography-SleepAI.

Home sleep apnea tests (HSATs) have emerged as alternatives to in-laboratory polysomnography (PSG), but Type IV HSATs often show limited diagnostic pe...