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Lawrence Hall, PhD



    • Cancer Biology and Evolution Program

Education & Training


  • Florida State University - MS - Mathematics
  • Florida State University - PhD - Computer Science

Continuing Education:

  • USAF Summer Faculty Research Program - Automated Target Recognition Branch, Wright Patterson AFB
  • Navy Summer Faculty Research Program - Naval Research Lab, Artificial Intelligence Center


  • NASA-Ames Research Center, Fellow -

Data mining from biomedical data is a general interest of Dr. Hall and his colleagues. Dr. Hall's work has included predicting the secondary structure of proteins, drug discovery, and designation of `'interesting'' areas in magnetic resonance images of the brain. He and his co-workers have participated in the Fourth and Fifth Community-wide Experiments on the Critical Assessment of Techniques for Protein Structure Prediction, known as CASP-4 and CASP-5. Dr. Hall's more specific research interests include intelligent symbolic systems (including development of an effective automated method to determine patient eligibility for clinical trials), machine learning, pattern recognition, and integration of artificial intelligence into medical image processing. He and his co-workers are working on a multiyear, multi-institution project to bring automatic segmentation of tissues in brain images into the clinic, with automatic tracking of brain tumors a specific goal. Exploitation of imprecision with the use of fuzzy logic in pattern recognition, artificial intelligence, and learning is a research theme. Parallel learning systems are another area of interest. Methods of scaling fuzzy clustering algorithms to large amounts of data for exploratory analysis and to facilitate distributed learning is an emphasis. Dr. Hall and his co-workers have developed distributed boosting methods and several distributed approaches to machine learning. They also have developed dynamic neural networks, modified approaches to building accurate decision trees, and placed a particular emphasis on developing effective methodswith ensembles of classifiers. An ensemble might include a support factor machine, a decision tree, a neural network, and a rule learner.


  • Ahmed KB, Goldgof GM, Paul R, Goldgof DB, Hall LO. Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification. IEEE Access. 2021 Jun.9:72970-72979. Pubmedid: 34178559. Pmcid: PMC8224464.
  • Dave P, Alahmari S, Goldgof D, Hall LO, Morera H, Mouton PR. An adaptive digital stain separation method for deep learning-based automatic cell profile counts. J Neurosci Meth. 2021 Apr.354:109102. Pubmedid: 33607171.
  • Moreno S, Bonfante M, Zurek E, Cherezov D, Goldgof D, Hall L, Schabath M. A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC. Tomography. 2021 Apr.7(2):154-168. Pubmedid: 33946756. Pmcid: PMC8162978.
  • Mu W, Liang Y, Hall LO, Tan Y, Balagurunathan Y, Wenham R, Wu N, Tian J, Gillies RJ. 18F-FDG PET/CT Habitat Radiomics Predicts Outcome of Patients with Cervical Cancer Treated with Chemoradiotherapy. Radiol Artif Intell. 2020 Nov.2(6):e190218. Pubmedid: 33937845. Pmcid: PMC8082355.
  • Paul R, Schabath MB, Gillies R, Hall LO, Goldgof DB. Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data. J Med Imaging (Bellingham). 2020 Mar.7(2):024502. Pubmedid: 32280729. Pmcid: PMC7134617.
  • Paul R, Shafiq-Ul Hassan M, Moros EG, Gillies RJ, Hall LO, Goldgof DB. Deep Feature Stability Analysis Using CT Images of a Physical Phantom Across Scanner Manufacturers, Cartridges, Pixel Sizes, and Slice Thickness. Tomography. 2020 Jun.6(2):250-260. Pubmedid: 32548303. Pmcid: PMC7289258.
  • Cherezov D, Paul R, Fetisov N, Gillies RJ, Schabath MB, Goldgof DB, Hall LO. Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's. Tomography. 2020 Jun.6(2):209-215. Pubmedid: 32548298. Pmcid: PMC7289250.
  • Paul R, Schabath M, Gillies R, Hall L, Goldgof D. Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future. Comput Biol Med. 2020 Jul.122:103882. Pubmedid: 32658721. Pmcid: PMC8108139.
  • Tunali I, Hall LO, Napel S, Cherezov D, Guvenis A, Gillies RJ, Schabath MB. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Med Phys. 2019 Sep.46(11):5075-5085. Pubmedid: 31494946. Pmcid: PMC6842054.
  • Alahmari SS, Goldgof D, Hall L, Phoulady HA, Patel RH, Mouton PR. Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology. J Chem Neuroanat. 2019 Mar.96:94-101. Pubmedid: 30594529.
  • Paul R, Schabath M, Balagurunathan Y, Liu Y, Li Q, Gillies R, Hall LO, Goldgof DB. Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features. Tomography. 2019 Mar.5(1):192-200. Pubmedid: 30854457. Pmcid: PMC6403047.
  • Cherezov D, Goldgof D, Hall L, Gillies R, Schabath M, Müller H, Depeursinge A. Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness. Sci Rep. 2019 Mar.9(1):4500. Pubmedid: 30872600. Pmcid: PMC6418269.
  • Ahmady Phoulady H, Goldgof D, Hall LO, Mouton PR. Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex. J Chem Neuroanat. 2019 Jul.98:1-7. Pubmedid: 30836126.
  • Alahmari SS, Cherezov D, Goldgof D, Hall L, Gillies RJ, Schabath MB. Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening. IEEE Access. 2019 Jan.6:77796-77806. Pubmedid: 30607311. Pmcid: PMC6312194.
  • Paul R, Hall L, Goldgof D, Schabath M, Gillies R. Predicting Nodule Malignancy using a CNN Ensemble Approach. Proc Int Jt Conf Neural Netw. 2018 Jul.2018. Pubmedid: 30443438. Pmcid: PMC6233309.
  • Paul R, Liu Y, Li Q, Hall L, Goldgof D, Balagurunathan Y, Schabath M, Gillies R. Representation of Deep Features using Radiologist defined Semantic Features. Proc Int Jt Conf Neural Netw. 2018 Jul.2018. Pubmedid: 30443437. Pmcid: PMC6233304.
  • Paul R, Hawkins SH, Schabath MB, Gillies RJ, Hall LO, Goldgof DB. Predicting malignant nodules by fusing deep features with classical radiomics features. J Med Imaging (Bellingham). 2018 Jan.5(1):011021. Pubmedid: 29594181. Pmcid: PMC5862127.
  • Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R. Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. Ajnr Am J Neuroradiol. 2018 Feb.39(2):208-216. Pubmedid: 28982791. Pmcid: PMC5812810.
  • Fang M, Yin J, Hall LO, Tao D. Active Multitask Learning With Trace Norm Regularization Based on Excess Risk. IEEE Trans Cybern. 2017 Nov.47(11):3906-3915. Pubmedid: 27479984.
  • Zhou M, Chaudhury B, Hall LO, Goldgof DB, Gillies RJ, Gatenby RA. Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction. J Magn Reson Imaging. 2017 Jul.46(1):115-123. Pubmedid: 27678245.
  • Phoulady HA, Goldgof D, Hall LO, Mouton PR. A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images. Comput Med Imag Grap. 2017 Jul.59:38-49. Pubmedid: 28701280.
  • Mouton PR, Phoulady HA, Goldgof D, Hall LO, Gordon M, Morgan D. Unbiased estimation of cell number using the automatic optical fractionator. J Chem Neuroanat. 2017 03.80:A1-A8. Pubmedid: 27988177. Pmcid: PMC5303677.
  • Cherezov D, Hawkins S, Goldgof D, Hall L, Balagurunathan Y, Gillies RJ, Schabath MB. Improving malignancy prediction through feature selection informed by nodule size ranges in NLST. Conf Proc IEEE Int Conf Syst Man Cybern. 2016 Oct.2016:001939-001944. Pubmedid: 30473607. Pmcid: PMC6251413.
  • Hawkins S, Wang H, Liu Y, Garcia A, Stringfield O, Krewer H, Li Q, Cherezov D, Gatenby RA, Balagurunathan Y, Goldgof D, Schabath MB, Hall L, Gillies RJ. Predicting Malignant Nodules from Screening CT Scans. J Thorac Oncol. 2016 Dec.11(12):2120-2128. Pubmedid: 27422797. Pmcid: PMC5545995.
  • Paul R, Hawkins SH, Balagurunathan Y, Schabath MB, Gillies RJ, Hall LO, Goldgof DB. Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma. Tomography. 2016 Dec.2(4):388-395. Pubmedid: 28066809. Pmcid: PMC5218828.
  • Chaudhury B, Zhou M, Goldgof DB, Hall LO, Gatenby RA, Gillies RJ, Patel BK, Weinfurtner RJ, Drukteinis JS. Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis. J Magn Reson Imaging. 2015 Nov.42(5):1421-1430. Pubmedid: 25884277. Pmcid: PMC5017794.
  • Parker JK, Hall LO. Accelerating Fuzzy-C Means Using an Estimated Subsample Size. IEEE Trans Fuzzy Syst. 2014 Oct.22(5):1229-1244. Pubmedid: 26617455. Pmcid: PMC4662382.
  • Balagurunathan Y, Gu Y, Wang H, Kumar V, Grove O, Hawkins S, Kim J, Goldgof DB, Hall LO, Gatenby RA, Gillies RJ. Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. Transl Oncol. 2014 Feb.7(1):72-87. Pubmedid: 24772210. Pmcid: PMC3998690.
  • Zhou M, Hall L, Goldgof D, Russo R, Balagurunathan Y, Gillies R, Gatenby R. Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol. 2014 Feb.7(1):5-13. Pubmedid: 24772202. Pmcid: PMC3998688.
  • Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B, Schwartz LH, Basu S, Eschrich S, Gatenby RA, Gillies RJ. Test-retest reproducibility analysis of lung CT image features. J Digit Imaging. 2014 Dec.27(6):805-823. Pubmedid: 24990346. Pmcid: PMC4391075.
  • Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, Bendtsen C, Velazquez ER, Dekker A, Aerts H, Lambin P, Li X, Tian J, Gatenby RA, Gillies RJ. Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach. Pattern Recognit. 2013 Mar.46(3):692-702. Pubmedid: 23459617. Pmcid: PMC3580869.
  • Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ. Radiomics: the process and the challenges. Magn Reson Imaging. 2012 Nov.30(9):1234-1248. Pubmedid: 22898692. Pmcid: PMC3563280.
  • Elozory DT, Kramer KA, Chaudhuri B, Bonam OP, Goldgof DB, Hall LO, Mouton PR. Automatic section thickness determination using an absolute gradient focus function. J Microsc. 2012 Dec.248(3):245-259. Pubmedid: 23078150. Pmcid: PMC4465598.
  • Hore P, Hall LO, Goldgof DB. A Scalable Framework For Cluster Ensembles. Pattern Recognit. 2009 May.42(5):676-688. Pubmedid: 20160846. Pmcid: PMC2654620.
  • Hore P, Hall LO, Goldgof DB, Gu Y, Maudsley AA, Darkazanli A. A Scalable Framework For Segmenting Magnetic Resonance Images. J Signal Process Syst. 2009 Jan.54(1-3):183-203. Pubmedid: 20046893. Pmcid: PMC2771942.
  • Kramer KA, Hall LO, Goldgof DB, Remsen A, Luo T. Fast support vector machines for continuous data. Ieee Trans Syst Man Cybern B Cybern. 2009 Aug.39(4):989-1001. Pubmedid: 19336328. Pmcid: PMC4467789.
  • Luo T, Kramer K, Goldgof D, Hall L, Samson S, Remsen A, Hopkins T. Active Learning to Recognize Multiple Types of Plankton. J Machine Learn Res. 2005.6:589-613. Pubmedid: noPMID.
  • Liu X, Hall L, Bowyer K. Comments on "a parallel mixture of SVMs for very large scale problems". Neural Comput. 2004 Jul.16(7):1345-1351. Pubmedid: 15165393.
  • Luo T, Kramer K, Goldgof D, Hall L, Samson S, Remsen A, Hopkins T. Recognizing plankton images from the shadow image particle profiling evaluation recorder. Ieee Trans Syst Man Cybern B Cybern. 2004 Aug.34(4):1753-1762. Pubmedid: 15462442.
  • Fink E, Kokku P, Nikiforou S, Hall L, Goldgof D, Krischer J. Selection of patients for clinical trials: an interactive web-based system. Artif Intell Med. 2004.31(3):241-254. Pubmedid: 15302090.
  • Chawla N, Hall L, Bowyer K, Kegelmeyer P. Learning ensembles from bites: A scalable and accurate approach. J Machine Learn Res. 2004.5:421-451. Pubmedid: noPMID.
  • Hall L, Bowyer K, Banfield R, Eschrich S, Collins R. Is Error-Based Pruning Redeemable?. Int J Artif Intell Tools. 2003 Sep.12(3):249-264.
  • Eschrich S, Ke J, Hall LO, Goldgof DB. Fast Accurate Fuzzy Clustering through Data Reduction. Ieee Trans Fuzzy Systems. 2003 Apr.11(2):262-270.
  • Chawla N, Moore T, Hall L, Bowyer K, Kegelmeyer W, Springer C. Distributed Learning with Bagging-Like Performance. Pat Recogn Lett. 2003.24(1-3):455-471. Pubmedid: noPMID.
  • Eschrich S, Chawla N, Hall L. Learning to predict in complex biological domains. J System Stimulation. 2002 Nov.14(11):1464-1471.
  • Chawla N, Bowyer K, Hall L, Kegelmeyer W. SMOTE: Synthetic minority over-sampling technique. J Artif Intel Res. 2002.16(2002):321-357. Pubmedid: noPMID.
  • Zhang M, Hall L, Goldgof D. A genetic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithm. Ieee Transactions On Systems, Man And Cybernetics. 2002.32(5):571-582. Pubmedid: noPMID.
  • Fletcher-Heath L, Hall L, Goldgof D, Reed F. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med. 2001 Jan.21(1-3):43-63. Pubmedid: 11154873.
  • Teodorescu HL, Kandel A, Hall LO. Report of research activities in fuzzy AI and medicine at USF CSE. Artif Intell Med. 2001 Jan.21(1-3):177-183. Pubmedid: 11154883.
  • Velthuizen R, Hall L, Clarke L. Feature extraction for MRI segmentation. J Neuroimaging. 1999 Apr.9(2):85-90. Pubmedid: 10208105.
  • Hall L, Ozyurt I, Bezdek J. Clustering with a genetically optimized approach. Ieee Trans Evol Comput. 1999.3(2):103-112. Pubmedid: noPMID.
  • Clark M, Hall L, Goldgof D, Velthuizen R, Silbiger M, Murtagh F. Automatic tumor segmentation using knowledge-based techniques. Ieee T Med Imaging. 1998 Apr.17(2):187-201. Pubmedid: 9688151.
  • Clarke L, Velthuizen R, Clark M, Gaviria G, Hall L, Goldgof D, Murtagh R, Phuphanich S, Brem S. MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation. Magn Reson Imaging. 1998 Apr.16(3):271-279. Pubmedid: 9621968.
  • Cheng T, Goldgof D, Hall L. Fast fuzzy clustering. Int J Fuzzy Sets Systems. 1998.93(1):49-56. Pubmedid: noPMID.
  • Ozyurt B, Sunol A, Camurdan M, Mogili P, Hall L. Chemical plant fault diagnosis through a hybrid symbolic connectionist approach and comparison with neural networks. Comput Chem Eng. 1998.22(1-2):299-321. Pubmedid: noPMID.
  • Hall L, Lande P. Generation of fuzzy rules from decision trees. J Adv Comput Intell. 1998.2(4):128-133. Pubmedid: noPMID.
  • Bezdek J, Hall L, Clark M, Goldgof D, Clarke L. Medical image analysis with fuzzy models. Stat Methods Med Res. 1997 Sep.6(3):191-214. Pubmedid: 9339497.
  • Vaidyanathan M, Clarke LP, Heidtman C, Velthuizen RP, Hall LO. Normal brain volume measurements using multispectral MRI segmentation. Magn Reson Imaging. 1997.15(1):87-97. Pubmedid: 9084029.
  • Velthuizen R, Hall L, Clarke L, Silbiger M. An investigation of mountain method clustering for large data sets. Pat Rec. 1997.30(7):1121-1135. Pubmedid: noPMID.
  • Hall L. Confirmation and denial as plausible modes of fuzzy inference. Fuzzy Sets And Systems. 1997.86(3):307-309. Pubmedid: noPMID.
  • Velthuizen R, Hall L, Clarke L. Feature extraction for MRI segmentation. Biomed Eng J. 1997.8(6):26-47. Pubmedid: noPMID.
  • Vaidyanathan M, Clarke LP, Hall LO, Heidtman C, Velthuizen R, Gosche K, Phuphanich S, Wagner, Jr. H, Greenberg H, Silbiger ML. Monitoring brain tumor response to therapy using MRI segmentation. Magn Reson Imaging. 1997.15(3):323-334. Pubmedid: 9201680.
  • Velthuizen R, Hall L, Clarke L. An initial investigation of feature extraction with genetic algorithms for fuzzy clustering. Biomed Eng. 1996.8(6):496-517.
  • Bensaid A, Bezdek J, Hall L, Clarke L. Partially supervised clustering for image segmentation. Pat Rec. 1996.29(5):559-571.
  • Bensaid A, Hall L, Bezdek J, Clarke L, Silbiger M, et. al.. Validity-guided (Re) clustering for image segmentation. Ieee Trans Fuzzy Systems. 1996.4(2):112-123.
  • Sanou K, Hall L, Ramaniuk S. An encoding of production rules in a connectionist network, journal of intelligent and fuzzy systems. J Intel Fuzzy Sys. 1996.4(1):1-18.
  • Velthuizen R, Clarke L, Phuphanich S, Hall L, Bensaid A, Arrington J, Greenberg H, Silbiger M. Unsupervised measurement of brain tumor volume on MR images. J Magn Reson Imaging. 1995 Sep.5(5):594-605. Pubmedid: 8574047.
  • Clarke L, Velthuizen R, Camacho M, Heine J, Vaidyanathan M, Hall L, Thatcher R, Silbiger M. MRI segmentation: methods and applications. Magn Reson Imaging. 1995.13(3):343-368. Pubmedid: 7791545.
  • Phillips W, Velthuizen R, Phuphanich S, Hall L, Clarke L, Silbiger M. Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. Magn Reson Imaging. 1995.13(2):277-290. Pubmedid: 7739370.
  • Vaidynathan M, Clarke L, Velthuizen R, Phuphanich S, Bensaid A, Hall L, Bezdek J, Greenberg H, Trotti, III A, Silbiger M. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Magn Reson Imaging. 1995.13(5):719-728. Pubmedid: 8569446.
  • Woods K, Cook D, Hall L, Stark L, Bowyer K. Learning membership functions in a function-based object recognition system. J Artif Intel Res. 1995.3:187-222. Pubmedid: noPMID.
  • Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition. Med Phys. 1993 Jul.20(4):1033-1048. Pubmedid: 8413011.
  • Bezdek J, Hall L, Clarke L. MR image segmentation techniques using pattern recognition. Med Phys. 1993.20(4):1033-1048.
  • Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw. 1992 Sep.3(5):672-682. Pubmedid: 18276467.
  • Hall L, Bensiad A, Clarke L, Velthuizen R, Silbiger M. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. Ieee Trans Med Imag. 1992.3(5):672-682.
  • Clark M, Hall L, Goldgof D, Silbiger M. Using fuzzy information in knowledge guided segmentation of brain tumors. In: Cabonell J, Siekman J, eds. Fuzzy logic in AI: towards intelligent systems. Springer Verlag; 1997;167-181.
  • Bezdek J, Hall L, Clark M, Goldgof D, Clarke L. Segmenting medica images with fuzzy models: an update. In: Dubois D, Prade H, Yager R, et al, eds. Fuzzy information engineering. New York, NY: Wiley; 1997;69-92.
  • Clark M, Hall L, Goldgof D, Velthuizen R, Murtagh F, Silbiger M. Unsupervised brain tumor segmentation using knowledge based and fuzzy techniques. In: Theodorescu H, Kandel A, Jain L, et al, eds. Fuzzy and Neuro-Fuzzy Systems in Medicine. Boca Raton, FL: CRC Press; 1999;137-169.