AIMC Topic: Lymphoma
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Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.
Communications biology
Oct 31, 2024
The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, cu...
The SINFONIA project repository for AI-based algorithms and health data.
Frontiers in public health
Oct 23, 2024
The SINFONIA project's main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental effects of radiation exposure on patients, workers, caretakers, and comforters, the public, and the env...
Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors.
International journal of medical informatics
Sep 14, 2024
BACKGROUND: New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhanceme...
A Vision Transformer-Based Framework for Knowledge Transfer From Multi-Modal to Mono-Modal Lymphoma Subtyping Models.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Determining lymphoma subtypes is a crucial step for better patient treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which relies on gene expression technology, is highly...
Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.
Neuroradiology
Sep 3, 2024
OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) a...
Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study.
Neuroradiology
Jul 17, 2024
PURPOSE: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI.
A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.
International journal of computer assisted radiology and surgery
Jul 14, 2024
PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D ...
Machine Learning for Automated Interpretation of Fluorodeoxyglucose-Positron Emission Tomography Scans in Lymphoma.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Jun 21, 2024
Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.
Journal of applied clinical medical physics
May 29, 2024
PURPOSE: This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma.
Adult
Prognosis
Male
Radiopharmaceuticals
Middle Aged
Female
Image Interpretation, Computer-Assisted
Deep Learning
Fluorodeoxyglucose F18
Humans
Aged
Signal-To-Noise Ratio
Aged, 80 and over
Image Processing, Computer-Assisted
Young Adult
Lymphoma
Positron Emission Tomography Computed Tomography
Whole Body Imaging
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