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Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review
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inferredcites →https://doi.org/10.1002/9780470050118.ecse614openalex-snapshot
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inferredcites →https://doi.org/10.1002/cyto.990130713openalex-snapshot
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inferredcites →https://doi.org/10.1002/cyto.a.22457openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-3-642-41181-6_62openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-981-15-0306-1_10openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-981-15-0798-4openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-981-16-1249-7_38openalex-snapshot
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inferredcites →https://doi.org/10.1007/b98879openalex-snapshot
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inferredcites →https://doi.org/10.1007/s00500-018-3288-5openalex-snapshot
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inferredcites →https://doi.org/10.1007/s00521-013-1438-3openalex-snapshot
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inferredcites →https://doi.org/10.1007/s00521-018-3359-7openalex-snapshot
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inferredcites →https://doi.org/10.1007/s10439-009-9866-zopenalex-snapshot
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inferredcites →https://doi.org/10.1007/s10916-018-1010-xopenalex-snapshot
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inferredcites →https://doi.org/10.1007/s10916-019-1219-3openalex-snapshot
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inferredcites →https://doi.org/10.1007/s11042-017-4478-3openalex-snapshot
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inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
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inferredcites →https://doi.org/10.1007/s11517-019-01984-1openalex-snapshot
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inferredcites →https://doi.org/10.1007/s12555-012-0393-6openalex-snapshot
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inferredcites →https://doi.org/10.1007/s13246-019-00742-9openalex-snapshot
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inferredcites →https://doi.org/10.1016/0031-3203(83)90016-xopenalex-snapshot
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inferredcites →https://doi.org/10.1016/0031-3203(92)90024-dopenalex-snapshot
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inferredcites →https://doi.org/10.1016/j.artmed.2012.03.005openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.artmed.2014.09.002openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.bbe.2017.07.003openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.bbe.2019.01.005openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.bbe.2020.02.005openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.bspc.2018.08.012openalex-snapshot
Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection
inferredcites →https://doi.org/10.1016/j.cmpb.2018.05.024openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.cmpb.2019.104987openalex-snapshot
Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images
inferredcites →https://doi.org/10.1016/j.eswa.2008.01.039openalex-snapshot
A simple and fast algorithm for K-medoids clustering
inferredcites →https://doi.org/10.1016/j.jss.2012.04.012openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.measurement.2017.11.002openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.media.2012.02.005openalex-snapshot
Machine learning and radiology
inferredcites →https://doi.org/10.1016/j.media.2016.11.004openalex-snapshot
DCAN: Deep contour-aware networks for object instance segmentation from histology images
inferredcites →https://doi.org/10.1016/j.micron.2010.04.017openalex-snapshot
Automated leukocyte recognition using fuzzy divergence
inferredcites →https://doi.org/10.1016/j.micron.2011.03.009openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.neucom.2019.09.083openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.patcog.2009.09.029openalex-snapshot
Shadowed c-means: Integrating fuzzy and rough clustering
inferredcites →https://doi.org/10.1016/j.patcog.2016.03.030openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.procs.2015.08.017openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0031-3025(16)38671-8openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0031-3203(97)00145-3openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
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inferredcites →https://doi.org/10.1038/s41592-019-0582-9openalex-snapshot
ilastik: interactive machine learning for (bio)image analysis
inferredcites →https://doi.org/10.1038/s41598-020-71294-2openalex-snapshot
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inferredcites →https://doi.org/10.1038/srep14938openalex-snapshot
An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images
inferredcites →https://doi.org/10.1049/iet-ipr.2016.0526openalex-snapshot
Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning
inferredcites →https://doi.org/10.1080/00034983.1948.11685347openalex-snapshot
Leishman's Stain Adapted for Use with Histological Sections
inferredcites →https://doi.org/10.1088/1742-6596/853/1/012011openalex-snapshot
Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features
inferredcites →https://doi.org/10.1093/ajcp/62.4.530openalex-snapshot
Evaluation of an Automated Differential Leukocyte Counting System: II. Normal Cell Identification
inferredcites →https://doi.org/10.1093/ajcp/62.4.537openalex-snapshot
Evaluation of an Automated Differential Leukocyte Counting System: III. Detection of Abnormal Cells
inferredcites →https://doi.org/10.1109/78.229897openalex-snapshot
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inferredcites →https://doi.org/10.1109/access.2016.2636218openalex-snapshot
Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities
inferredcites →https://doi.org/10.1109/acsat.2013.80openalex-snapshot
Image Segmentation Using an Adaptive Clustering Technique for the Detection of Acute Leukemia Blood Cells Images
inferredcites →https://doi.org/10.1109/aipr41111.2017openalex-snapshot
2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
inferredcites →https://doi.org/10.1109/asicon.2017.8252657openalex-snapshot
Automatic classification of leukocytes using deep neural network
inferredcites →https://doi.org/10.1109/bmei.2008.262openalex-snapshot
A New Approach for Touching Cells Segmentation
inferredcites →https://doi.org/10.1109/c-code.2019.8680972openalex-snapshot
Computer-assisted Acute Lymphoblastic Leukemia detection and diagnosis
inferredcites →https://doi.org/10.1109/calcon.2017.8280777openalex-snapshot
An efficient algorithm for segregation of white and red blood cells based on modified hough transform
inferredcites →https://doi.org/10.1109/ccece.2013.6567770openalex-snapshot
Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding
inferredcites →https://doi.org/10.1109/ciet.2018.8660914openalex-snapshot
Automatic Detection of White Blood Cells from Microscopic Images for Malignancy Classification of Acute Lymphoblastic Leukemia
inferredcites →https://doi.org/10.1109/codit.2019.8820299openalex-snapshot
Automatic Acute Lymphoblastic Leukemia Detection and Comparative Analysis from Images
inferredcites →https://doi.org/10.1109/cspa18771.2011openalex-snapshot
2011 IEEE 7th International Colloquium on Signal Processing and its Applications
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvprw.2017.111openalex-snapshot
Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning
inferredcites →https://doi.org/10.1109/dicta.2016.7797097openalex-snapshot
White Blood Cell Nuclei Segmentation Using Level Set Methods and Geometric Active Contours
inferredcites →https://doi.org/10.1109/fcv35649.2015openalex-snapshot
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)
inferredcites →https://doi.org/10.1109/icaca40133.2016openalex-snapshot
2016 IEEE International Conference on Advances in Computer Applications (ICACA)
inferredcites →https://doi.org/10.1109/icacci.2014.6968362openalex-snapshot
Classification of white blood cells based on morphological features
inferredcites →https://doi.org/10.1109/icacci.2017.8126106openalex-snapshot
Automatic detection of acute myeloid leukemia from microscopic blood smear image
inferredcites →https://doi.org/10.1109/icacci.2018.8554576openalex-snapshot
Identification of Acute Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine Learning Algorithms
inferredcites →https://doi.org/10.1109/icasi.2017.7988618openalex-snapshot
HeMatic: An automated leukemia detector with separation of overlapping blood cells through Image Processing and Genetic Algorithm
inferredcites →https://doi.org/10.1109/icassp39728.2021.9414362openalex-snapshot
Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning
inferredcites →https://doi.org/10.1109/icces.2017.8275387openalex-snapshot
Elephant herd optimization with neural networks: A case study on acute Lymphoblastic Leukemia diagnosis
inferredcites →https://doi.org/10.1109/iccia.2018.00036openalex-snapshot
Using Convolutional Neural Networks for Automated Fine Grained Image Classification of Acute Lymphoblastic Leukemia
inferredcites →https://doi.org/10.1109/iccpct.2014.7055057openalex-snapshot
Modified segmentation algorithm and its feature extraction of cancer affected white blood cells
inferredcites →https://doi.org/10.1109/icdecom.2011.5738491openalex-snapshot
Fuzzy Based Blood Image Segmentation for Automated Leukemia Detection
inferredcites →https://doi.org/10.1109/icecome.2018.8644754openalex-snapshot
Blood Cell Images Segmentation using Deep Learning Semantic Segmentation
inferredcites →https://doi.org/10.1109/iced.2016.7804693openalex-snapshot
Classification of bone marrow acute leukemia cells using multilayer perceptron network
inferredcites →https://doi.org/10.1109/icemi46757.2019.9101445openalex-snapshot
Improved faster RCNN for white blood cells detection in blood smear image
inferredcites →https://doi.org/10.1109/iciev.2016.7760026openalex-snapshot
White blood cells nucleus segmentation from microscopic images of strained peripheral blood film during leukemia and normal condition
inferredcites →https://doi.org/10.1109/iciinfs.2017.8300425openalex-snapshot
Simultaneous localization and classification of acute lymphoblastic leukemic cells in peripheral blood smears using a deep convolutional network with average pooling layer
inferredcites →https://doi.org/10.1109/icip.2011.6115881openalex-snapshot
All-IDB: The acute lymphoblastic leukemia image database for image processing
inferredcites →https://doi.org/10.1109/icisce50968.2020.00301openalex-snapshot
White blood cell segmentation based on dual path network and channel attention
inferredcites →https://doi.org/10.1109/icnc.2007.293openalex-snapshot
Cell Cluster Image Segmentation on Form Analysis
inferredcites →https://doi.org/10.1109/icoei.2017.8300983openalex-snapshot
Detection of leukemia and its types using image processing and machine learning
inferredcites →https://doi.org/10.1109/icoiact.2018.8350822openalex-snapshot
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inferredcites →https://doi.org/10.1109/icoias.2018.8493721openalex-snapshot
Automatic White Blood Cell Segmentation Based on Color Segmentation and Active Contour Model
inferredcites →https://doi.org/10.1109/icosp.2010.5655754openalex-snapshot
Classification of three types of red blood cells in peripheral blood smear based on morphology
inferredcites →https://doi.org/10.1109/icpr.2008.4761451openalex-snapshot
Segmentation of overlapping/aggregating nuclei cells in biological images
inferredcites →https://doi.org/10.1109/icpr40739.2016openalex-snapshot
2016 23rd International Conference on Pattern Recognition (ICPR)
inferredcites →https://doi.org/10.1109/icsmb.2010.5735344openalex-snapshot
Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images
inferredcites →https://doi.org/10.1109/icsmc.2012.6377762openalex-snapshot
Deterministic model for Acute Myelogenous Leukemia classification
inferredcites →https://doi.org/10.1109/ictc46691.2019.8939959openalex-snapshot
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification
inferredcites →https://doi.org/10.1109/ictke.2018.8612303openalex-snapshot
Leukemia Prediction from Microscopic Images of Human Blood Cell Using HOG Feature Descriptor and Logistic Regression
inferredcites →https://doi.org/10.1109/icts.2016.7910275openalex-snapshot
Computer-aided screening for Acute Leukemia blood infection using gray-level intensity
inferredcites →https://doi.org/10.1109/iecbes.2010.5742239openalex-snapshot
Leukocyte's nucleus segmentation using active contour in YCbCr colour space
inferredcites →https://doi.org/10.1109/iecr.2010.5720171openalex-snapshot
Image analysis of blood microscopic images for acute leukemia detection
inferredcites →https://doi.org/10.1109/ines.2018.8523900openalex-snapshot
Leukemia Diagnosis Using Image Processing and Computational Intelligence
inferredcites →https://doi.org/10.1109/infop.2015.7489422openalex-snapshot
Detection of leukemia in microscopic white blood cell images
inferredcites →https://doi.org/10.1109/infop.2015.7489435openalex-snapshot
Blood cell segmentation from microscopic blood images
inferredcites →https://doi.org/10.1109/interact.2010.5706196openalex-snapshot
Automated leukemia detection using hausdorff dimension in blood microscopic images
inferredcites →https://doi.org/10.1109/isspa.2010.5605410openalex-snapshot
Comparison of acute leukemia Image segmentation using HSI and RGB color space
inferredcites →https://doi.org/10.1109/isspit37324.2015openalex-snapshot
2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
inferredcites →https://doi.org/10.1109/iwdrl.2018.8358214openalex-snapshot
Automated detection of white blood cells cancer diseases
inferredcites →https://doi.org/10.1109/jsyst.2014.2308452openalex-snapshot
Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images
inferredcites →https://doi.org/10.1109/mcas.2006.1688199openalex-snapshot
Ensemble based systems in decision making
inferredcites →https://doi.org/10.1109/medcom.2014.7005992openalex-snapshot
Identification and classification of acute leukemia using neural network
inferredcites →https://doi.org/10.1109/pcitc.2015.7438079openalex-snapshot
Robust technique for the detection of Acute Lymphoblastic Leukemia
inferredcites →https://doi.org/10.1109/rteict.2016.7807865openalex-snapshot
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inferredcites →https://doi.org/10.1109/rteict.2017.8256613openalex-snapshot
Computer aided leukemia detection using digital image processing techniques
inferredcites →https://doi.org/10.1109/sibgrapi-t19670.2011openalex-snapshot
2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials
inferredcites →https://doi.org/10.1109/sibgrapi.2017.55openalex-snapshot
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inferredcites →https://doi.org/10.1109/tbme.1976.324651openalex-snapshot
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inferredcites →https://doi.org/10.1109/tbme.2008.2008635openalex-snapshot
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inferredcites →https://doi.org/10.1109/tbme.2010.2060486openalex-snapshot
Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification
inferredcites →https://doi.org/10.1109/tcsi.2006.884469openalex-snapshot
Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy
inferredcites →https://doi.org/10.1109/titb.2012.2207398openalex-snapshot
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inferredcites →https://doi.org/10.1109/tmi.2007.896925openalex-snapshot
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inferredcites →https://doi.org/10.1109/tmi.2016.2538465openalex-snapshot
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inferredcites →https://doi.org/10.1109/tpami.1984.4767591openalex-snapshot
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inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
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inferredcites →https://doi.org/10.1111/ijlh.13042openalex-snapshot
Digital morphology analyzers in hematology: ICSH review and recommendations
inferredcites →https://doi.org/10.1111/j.1346-8138.2011.01335.xopenalex-snapshot
Single step modified ink staining for Tzanck test: Quick detection of herpetic giant cells in Tzanck smear
inferredcites →https://doi.org/10.1111/j.1365-2141.1977.tb00614.xopenalex-snapshot
AUTOMATED DIFFERENTIAL LEUCOCYTE COUNTING: THE PRESENT STATE OF THE ART
inferredcites →https://doi.org/10.11113/jt.v74.4675openalex-snapshot
Segmentation of White Blood Cell Nucleus Using Active Contour
inferredcites →https://doi.org/10.1117/1.jei.23.6.063017openalex-snapshot
Efficient contrast enhancement through log-power histogram modification
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A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images
inferredcites →https://doi.org/10.1117/12.2311282openalex-snapshot
Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception
inferredcites →https://doi.org/10.1117/12.878748openalex-snapshot
Counting of RBCs and WBCs in noisy normal blood smear microscopic images
inferredcites →https://doi.org/10.1117/3.831079.ch3openalex-snapshot
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inferredcites →https://doi.org/10.1136/bmj.n71openalex-snapshot
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
inferredcites →https://doi.org/10.1142/s0218001400000684openalex-snapshot
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TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES
inferredcites →https://doi.org/10.1145/3009977.3010043openalex-snapshot
Overlapping cell nuclei segmentation in microscopic images using deep belief networks
inferredcites →https://doi.org/10.1155/2016/9514707openalex-snapshot
Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method
inferredcites →https://doi.org/10.1166/jmihi.2012.1099openalex-snapshot
White Blood Cell Segmentation for Acute Leukemia Bone Marrow Images
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The Giemsa Stain: Its History and Applications
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Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1177/24.1.1254917openalex-snapshot
Performance of the LARC classifier in clinical laboratories.
inferredcites →https://doi.org/10.1177/25.7.70454openalex-snapshot
Automatic measurement of sister chromatid exchange frequency.
inferredcites →https://doi.org/10.1186/gb-2006-7-10-r100openalex-snapshot
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
inferredcites →https://doi.org/10.12792/iciae2015.051openalex-snapshot
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Deep learning approach to peripheral leukocyte recognition
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A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN Classifier
inferredcites →https://doi.org/10.21595/jve.2015.16124openalex-snapshot
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inferredcites →https://doi.org/10.3233/thc-161133openalex-snapshot
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inferredcites →https://doi.org/10.3343/alm.2013.33.1.1openalex-snapshot
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inferredcites →https://doi.org/10.3390/computers9020029openalex-snapshot
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inferredcites →https://doi.org/10.3390/diagnostics9030104openalex-snapshot
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inferredcites →https://doi.org/10.3390/electronics9010188openalex-snapshot
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inferredcites →https://doi.org/10.4103/2153-3539.93895openalex-snapshot
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inferredcites →https://doi.org/10.4103/2228-7477.150428openalex-snapshot
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inferredcites →https://doi.org/10.4103/2228-7477.205503openalex-snapshot
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inferredcites →https://doi.org/10.4103/2277-9175.163998openalex-snapshot
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inferredcites →https://doi.org/10.4103/jmss.jmss_7_17openalex-snapshot
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inferredcites →https://doi.org/10.4236/jsea.2012.512b020openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1805.09501openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1810.07961openalex-snapshot
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inferredcites →https://doi.org/10.5120/ijais2015451461openalex-snapshot
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inferredcites →https://doi.org/10.5121/csit.2017.71305openalex-snapshot
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inferredcites →https://doi.org/10.5267/j.ijiec.2015.8.004openalex-snapshot
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inferredcites →https://doi.org/10.5555/1005332.1005336openalex-snapshot
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inferredcites →https://doi.org/10.5772/6237openalex-snapshot
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inferredcites →https://doi.org/10.7937/tcia.2019.36f5o9ldopenalex-snapshot
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inferredcites →https://doi.org/10.7937/tcia.2019.b6foe619openalex-snapshot
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inferredcites →https://doi.org/10.7937/tcia.2019.of2w8lxropenalex-snapshot
SN-CanData: White Blood Cancer Dataset of B-ALL and MM for Stain Normalization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/jbio.202500121openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/prm2.70014openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-032-02979-9_7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00521-023-09157-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11277-023-10424-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11831-026-10512-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41870-024-01990-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bea.2025.100202openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2024.125390openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126725openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neunet.2023.11.006openalex-snapshot
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inferred← citeshttps://doi.org/10.1049/htl2.70091openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2023.3289219openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/c2i663243.2024.10895701openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/comnetsat59769.2023.10420732openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iceeie66203.2025.11252010openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icrai62391.2024.10894459openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/raics61201.2024.10689773openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/exsy.13225openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/jebm.70005openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12880-022-00801-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s43162-026-00746-4openalex-snapshot
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inferred← citeshttps://doi.org/10.13005/bpj/2842openalex-snapshot
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inferred← citeshttps://doi.org/10.17488/rmib.43.2.3openalex-snapshot
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inferred← citeshttps://doi.org/10.26599/nbe.2023.9290014openalex-snapshot
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inferred← citeshttps://doi.org/10.32604/cmes.2023.030704openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/app132112068openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/s26196290openalex-snapshot
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inferred← citeshttps://doi.org/10.53022/oarjet.2023.4.2.0060openalex-snapshot
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inferred← citeshttps://doi.org/10.55544/sjmars.2.4.5openalex-snapshot
Artificial Intelligence in Automated Testing Environments
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