21.4.7.1 Blood Cell Cancers, Lymphoma, Leukemia

Chapter Contents (Back)
Cells. Leukemia. Blood Cells. Lymphoma.

Landeweerd, G.H., Gelsema, E.S., Bins, M., Halie, M.R.,
Interactive pattern recognition of blood cells in malignant lymphomas,
PR(14), No. 1-6, 1981, pp. 239-244.
Elsevier DOI 0309
BibRef

Olson, G.B.[George B.], Bartels, P.H.[Peter H.],
Computer discrimination of splenocytes and peripheral blood lymphocytes from mice infected with friend murine leukemia virus,
PR(13), No. 1, 1981, pp. 57-64.
Elsevier DOI 0309
BibRef

Aus, H.M., Harms, H., Haucke, M., Beritova, J., ter Meulen, V., Gunzer, U., Baumann, I., Abmayr, W.,
Statistical Evaluation of Computer Markers to Detect Leukemias,
PRL(4), 1986, pp. 231-241. BibRef 8600

Firestone, L.M., Preston, K., Nathwani, B.N.,
Continuous Class Pattern-Recognition for Pathology, with Applications to Non-Hodgkins Follicular Lymphomas,
PR(29), No. 12, December 1996, pp. 2061-2078.
Elsevier DOI 9701
BibRef

Yeh, J.R., Lin, C.W., Shieh, J.S.,
An Approach of Multiscale Complexity in Texture Analysis of Lymphomas,
SPLetters(18), No. 4, April 2011, pp. 239-242.
IEEE DOI 1103
BibRef

Dimitropoulos, K.[Kosmas], Michail, E.[Emmanouil], Koletsa, T.[Triantafyllia], Kostopoulos, I.[Ioannis], Grammalidis, N.[Nikos],
Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma,
SIViP(8), No. S1, December 2014, pp. 33-40.
Springer DOI 1411
BibRef

Bozkurt, A.[Alican], Suhre, A.[Alexander], Cetin, A.E.[A. Enis],
Multi-scale directional-filtering-based method for follicular lymphoma grading,
SIViP(8), No. S1, December 2014, pp. 63-70.
WWW Link. 1411
BibRef
And: Erratum: SIViP(8), No. S1, December 2014, pp. 71.
WWW Link. 1411
BibRef

Dimitropoulos, K.[Kosmas], Barmpoutis, P.[Panagiotis], Kostopoulos, I.[Ioannis], Grammalidis, N.[Nikos],
Automated detection and classification of nuclei in PAX5 and H&E-stained tissue sections of follicular lymphoma,
SIViP(11), No. 1, January 2017, pp. 145-153.
WWW Link. 1702
BibRef

Al-jaboriy, S.S.[Saif S.], Sjarif, N.N.A.[Nilam Nur Amir], Chuprat, S.[Suriayati], Abduallah, W.M.[Wafaa Mustafa],
Acute lymphoblastic leukemia segmentation using local pixel information,
PRL(125), 2019, pp. 85-90.
Elsevier DOI 1909
Acute lymphoblastic leukemia, Machine learning technique, Segmentation, 4-moment statistical features, Microscopy images BibRef

Roy, R.M.[Reena M.], Ameer, P.M.,
Identification of white blood cells for the diagnosis of acute myeloid leukemia,
IJIST(32), No. 4, 2022, pp. 1307-1317.
DOI Link 2207
acute myeloid leukemia, classification, ensemble learning, leukocyte, transfer learning BibRef

Baby, D.[Diana], Juliet, S.[Sujitha], Raj, M.M.A.[M. M. Anishin],
An efficient lymphocytic leukemia detection based on EfficientNets and ensemble voting classifier,
IJIST(33), No. 1, 2023, pp. 419-426.
DOI Link 2301
classification, EfficientNet, leukemia detection, transfer learning, voting classifier BibRef

Gao, Z.[Zeyu], Mao, A.[Anyu], Wu, K.[Kefei], Li, Y.[Yang], Zhao, L.[Liebin], Zhang, X.L.[Xian-Li], Wu, J.[Jialun], Yu, L.[Lisha], Xing, C.[Chao], Gong, T.[Tieliang], Zheng, Y.F.[Ye-Feng], Meng, D.Y.[De-Yu], Zhou, M.[Min], Li, C.[Chen],
Childhood Leukemia Classification via Information Bottleneck Enhanced Hierarchical Multi-Instance Learning,
MedImg(42), No. 8, August 2023, pp. 2348-2359.
IEEE DOI 2308
Annotations, Pediatrics, Feature extraction, Hospitals, Task analysis, Costs, Correlation, Acute leukemia, hierarchical classification BibRef

Salvi, M.[Massimo], Michielli, N.[Nicola], Meiburger, K.M.[Kristen M.], Cattelino, C.[Cristina], Cotrufo, B.[Bruna], Giacosa, M.[Matteo], Giovanzana, C.[Chiara], Molinari, F.[Filippo],
cyto-Knet: An instance segmentation approach for multiple myeloma plasma cells using conditional kernels,
IJIST(34), No. 1, 2024, pp. e22984.
DOI Link 2401
cytology, deep learning, Giemsa stain, instance segmentation, myeloma plasma cells BibRef


Kockwelp, J.[Jacqueline], Thiele, S.[Sebastian], Kockwelp, P.[Pascal], Bartsch, J.[Jannis], Schliemann, C.[Christoph], Angenendt, L.[Linus], Risse, B.[Benjamin],
Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia,
CVMI22(1824-1833)
IEEE DOI 2210
Deep learning, Pathology, Image analysis, Pipelines, Manuals BibRef

Dhalla, S.[Sabrina], Mittal, A.[Ajay], Gupta, S.[Savita], Singh, H.[Harleen],
Multi-model Ensemble to Classify Acute Lymphoblastic Leukemia in Blood Smear Images,
AIHA20(243-253).
Springer DOI 2103
BibRef

Nava, R., González, G., Kybic, J., Escalante-Ramírez, B.,
Characterization of hematologic malignancies based on discrete orthogonal moments,
IPTA16(1-6)
IEEE DOI 1703
blood BibRef

Martinez-Martinez, F., Kybic, J., Lambert, L.,
Automatic detection of bone marrow infiltration by multiple myeloma detection in low-dose CT,
ICIP15(4813-4817)
IEEE DOI 1512
Myeloma; automatic; detection; infiltration; scalloping BibRef

Sarrafzadeh, O.[Omid], Rabbani, H.[Hossein], Dehnavi, A.M.[Alireza Mehri], Talebi, A.[Ardeshir],
Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation,
ICIP15(3339-3343)
IEEE DOI 1512
Bone Marrow Microscopic Image BibRef

di Ruberto, C.[Cecilia], Fodde, G.[Giuseppe], Putzu, L.[Lorenzo],
On Different Colour Spaces for Medical Colour Image Classification,
CAIP15(I:477-488).
Springer DOI 1511
BibRef
Earlier:
Comparison of Statistical Features for Medical Colour Image Classification,
CVS15(3-13).
Springer DOI 1507
BibRef
Earlier: A1, A3, Only:
Investigation of Different Classification Models to Determine the Presence of Leukemia in Peripheral Blood Image,
CIAP13(I:612-621).
Springer DOI 1311
BibRef

Vincent, I., Kwon, K.R.[Ki-Ryong], Lee, S.H.[Suk-Hwan], Moon, K.S.[Kwang-Seok],
Acute lymphoid leukemia classification using two-step neural network classifier,
FCV15(1-4)
IEEE DOI 1506
cancer BibRef

Desbordes, P., Petitjean, C., Ruan, S.[Su],
3D automated lymphoma segmentation in PET images based on cellular automata,
IPTA14(1-6)
IEEE DOI 1503
cellular automata BibRef

Labati, R.D.[Ruggero Donida], Piuri, V.[Vincenzo], Scotti, F.[Fabio],
All-IDB: The acute lymphoblastic leukemia image database for image processing,
ICIP11(2045-2048).
IEEE DOI 1201
BibRef

Wang, J.Y.[Ji-Yong], Xia, Y.[Yong], Wang, J.[Jiabin], Feng, D.D.,
Variational Bayes Inference Based Segmentation of Heterogeneous Lymphoma Volumes in Dual-Modality PET-CT Images,
DICTA11(274-278).
IEEE DOI 1205
BibRef

Sertel, O.[Olcay], Catalyurek, U.V.[Umit V.], Lozanski, G.[Gerard], Shanaah, A.[Arwa], Gurcan, M.N.[Metin N.],
An Image Analysis Approach for Detecting Malignant Cells in Digitized H&E-stained Histology Images of Follicular Lymphoma,
ICPR10(273-276).
IEEE DOI 1008
BibRef

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Malaria Detection, Analysis .


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