AIMC Topic: Lymphoma

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Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images.

Computer methods and programs in biomedicine Aug 4, 2021
BACKGROUND: After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based com...
Algorithms Support Vector Machine Lymphoma Magnetic Resonance Imaging Humans Neural Networks, Computer Machine Learning
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Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks.

Scientific reports Jul 26, 2021
A subset of primary central nervous system lymphomas (PCNSL) are difficult to distinguish from glioblastoma multiforme (GBM) on magnetic resonance imaging (MRI). We developed a convolutional neural network (CNN) to distinguish these tumors on contras...
Lymphoma Magnetic Resonance Imaging Diagnosis, Differential Glioblastoma Brain Neoplasms Male Middle Aged Neural Networks, Computer Machine Learning Young Adult Aged Female Humans Adult Aged, 80 and over
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A Machine Learning Tool Using Digital Microscopy (Morphogo) for the Identification of Abnormal Lymphocytes in the Bone Marrow.

Acta cytologica Jul 20, 2021
Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver va...
Bone Marrow Examination Diagnosis, Computer-Assisted Lymphoma Predictive Value of Tests Microscopy Image Interpretation, Computer-Assisted Lymphocytes Bone Marrow Cells Humans Machine Learning Retrospective Studies Reproducibility of Results
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Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.

Journal of magnetic resonance imaging : JMRI Mar 11, 2021
BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies.
Magnetic Resonance Imaging Diagnosis, Differential Glioblastoma Central Nervous System Middle Aged Deep Learning Lymphoma Humans Female Male Neural Networks, Computer Retrospective Studies
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Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.

Radiology Mar 9, 2021
Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing brain MRI scans using generative adversarial networks (GANs) allows for ...
Aged Brain Neoplasms Male Middle Aged Humans Retrospective Studies Female Contrast Media Magnetic Resonance Imaging Glioblastoma Deep Learning Image Processing, Computer-Assisted Lymphoma
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Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders.

Pathology Feb 25, 2021
Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematologi...
Leukemia Hematology Machine Learning Lymphoma Flow Cytometry Humans Artificial Intelligence
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Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.

Nature communications Dec 1, 2020
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through...
Humans Machine Learning Cell Line, Tumor Lymphoma Precision Medicine Antineoplastic Agents Drug Interactions Bortezomib Crizotinib
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Comparison of 11 automated PET segmentation methods in lymphoma.

Physics in medicine and biology Nov 27, 2020
Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesions and quantifying patient disease burden. Simple thresholding methods remain common despite the large heterogeneity in lymphoma lesion location, size...
Fluorodeoxyglucose F18 Positron Emission Tomography Computed Tomography Radiopharmaceuticals Tumor Burden Humans Male Middle Aged Lymphoma Female Aged Young Adult Adult Neural Networks, Computer Algorithms Retrospective Studies
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Editorial for: "Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning".

Journal of magnetic resonance imaging : JMRI Aug 7, 2020
Lymphoma Central Nervous System Multiparametric Magnetic Resonance Imaging Humans Deep Learning Image Processing, Computer-Assisted
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Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

Laboratory investigation; a journal of technical methods and pathology May 29, 2020
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including ...
Lymphoma Lymphoma, Large B-Cell, Diffuse Lymphoma, Follicular Observer Variation Diagnosis, Computer-Assisted Pathologists Humans Pseudolymphoma Image Interpretation, Computer-Assisted Deep Learning Neural Networks, Computer Histological Techniques Algorithms
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