Digital pathology image analysis: opportunities and challenges

par Madabhushi, Anant
Publié dans Imaging Med (2009)

Graphical processing unit implementation of an integrated shape-based active contour: Application to digital pathology

par Ali, Sahirzeeshan, Madabhushi, Anant
Publié 2012

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

par Viswanath, Satish, Madabhushi, Anant
Publié 2012

Statistical Shape Model for Manifold Regularization: Gleason grading of prostate histology

par Sparks, Rachel, Madabhushi, Anant
Publié 2013

Explicit shape descriptors: Novel morphologic features for histopathology classification

par Sparks, Rachel, Madabhushi, Anant
Publié 2013

Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images

par Sparks, Rachel, Madabhushi, Anant
Publié dans Sci Rep (2016)

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

par Janowczyk, Andrew, Madabhushi, Anant
Publié dans J Pathol Inform (2016)

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

par Bhargava, Rohit, Madabhushi, Anant
Publié dans Annu Rev Biomed Eng (2016)

Image analysis and machine learning in digital pathology: Challenges and opportunities

par Madabhushi, Anant, Lee, George
Publié dans Med Image Anal (2016)

Class-Specific Weighting for Markov Random Field Estimation: Application to Medical Image Segmentation

par Monaco, James P., Madabhushi, Anant
Publié 2012

A Hierarchical Unsupervised Spectral Clustering Scheme for Detection of Prostate Cancer from Magnetic Resonance Spectroscopy (MRS)

par Tiwari, Pallavi, Madabhushi, Anant, Rosen, Mark
Publié 2007

Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene- and Protein-Expression Studies

par Lee, George, Rodriguez, Carlos, Madabhushi, Anant
Publié 2008

A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS)

par Tiwari, Pallavi, Rosen, Mark, Madabhushi, Anant
Publié 2009

Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions

par Janowczyk, Andrew, Chandran, Sharat, Madabhushi, Anant
Publié 2013

Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS

par Tiwari, Pallavi, Kurhanewicz, John, Madabhushi, Anant
Publié 2012

Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management

par Tiwari, Pallavi, Danish, Shabbar, Madabhushi, Anant
Publié 2014

Identifying MRI Markers Associated with Early Response following Laser Ablation for Neurological Disorders: Preliminary Findings

par Tiwari, Pallavi, Danish, Shabbar, Madabhushi, Anant
Publié dans PLoS One (2014)

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

par Basavanhally, Ajay, Viswanath, Satish, Madabhushi, Anant
Publié dans PLoS One (2015)

Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces

par Sridhar, Akshay, Doyle, Scott, Madabhushi, Anant
Publié dans J Pathol Inform (2015)

Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings

par Toth, Robert, Sperling, Dan, Madabhushi, Anant
Publié dans PLoS One (2016)