Farzad Khalvati
Associate Professor
Leads the IMICS lab, developing deep learning for medical image analysis.
Farzad Khalvati's lab at Toronto works on medical imaging & healthcare AI. 45 papers since 2023, mostly npj Precision Oncology. 3 PhD students and 6 master's students. 47 alumni. Applications close .
At a glance
- papers, 2023–2026
- 12/ year
- at T1 venues
- 0%
- PhD students
- 3
- MS students
- 6
- alumni listed
- 47
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 0 · collecting
- 11 papers2025 · T1 0
- 18 papers2024 · T1 0
- 10 papers2023 · T1 0
T1 venuesOtherVenues: npj Precision Oncology
Research focuspaper titles that use each topic's words
- Medical imaging & healthcare AI8 papers
- medical3 papers
- medical imaging0 papers
- image analysis0 papers
- developing0 papers
Paper list
From the lab homepage.
20266 papers
- Proceedings of the International Society for Optics and Photonics, SPIE Medical ImagingImproving machine‑learning‑based multiclass pediatric low‑grade glioma molecular subtype identification using large language models
- Proceedings of the International Society for Optics and Photonics, SPIE Medical ImagingImproving pediatric low‑grade glioma molecular subtype identification using a novel AUROC loss function for convolutional neural networks
- Neuro-Oncology AdvancesIntegrating chemotherapy, radiotherapy and MGMT status with deep-learning cellular tumor volumetry sharpens prediction of glioblastoma recurrence on post-operative MRI
- American Journal of NeuroradiologyMulticlass radiomics-based prediction of BRAF mutation status in pediatric low-grade gliomas using multisequence MRI
- Frontiers in Neurologymultimodal machine learning for distinguishing pediatric multiple sclerosis from non-inflammatory conditions using optical coherence tomography
- EMBCProtoTopic: Prototypical network for few-shot medical topic modeling
202511 papers
- BMC Medical ImagingDeep superpixel generation and clustering for weakly supervised segmentations of brain tumors in MR Images
- BHIExploring the impact of supervised multimodal learning on the performance and explainability of pediatric brain tumor molecular diagnosis
- Seminars in NeurologyFederated Learning in Neurology: Bridging data privacy and artificial intelligence for brain health
- Scientific ReportsGenerative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images
- npj Precision OncologyMulticlass machine learning models for molecular subtype identification of pediatric low-grade glioma using bi-institutional MRIs for precision medicine
- Scientific ReportsMultimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis
- BMC Medical ImagingNon-invasive liver fibrosis screening on CT images using radiomics
- BMC Medical ImagingOpen-radiomics: A collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines
- Frontiers in Artificial Intelligence-Medicine and Public HealthPredicting pediatric diagnostic imaging patient no-show and extended wait-times using LLMs, regression, and tree based models
- ResuscitationSeeing beyond the scan: Artificial Intelligence and the future of detecting hypoxic-ischemic brain injury in children
- Communications MedicineSegmentation-free pretherapeutic assessment of BRAF-status in pediatric low-grade gliomas
202418 papers
- Canadian Association of Radiologists JournalA correlative assessment of machine learning-based cobb angle measurements and human-based measurements in adolescent idiopathic and congenital scoliosis
- BHIAdvanced receiver operating characteristic curve analysis to identify outliers in binary machine learning classifications for precision medicine
- EMBCAnomaly detection in pediatric and adults brain MRI with generative Model
- Child’s Nervous SystemApplications of machine learning to MR imaging of pediatric low-grade gliomas
- Diagnostic and Interventional ImagingArtificial Intelligence in emergency neuroradiology: Current applications and perspectives
- American Journal of NeuroradiologyArtificial Intelligence in the future landscape of pediatric neuroradiology: opportunities and challenges (opens in a new tab)
- EMBCAutomating cobb angle measurement for adolescent idiopathic scoliosis using instance segmentation
- Scientific ReportsBeyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas
- Proceedings of Machine Learning ResearchConditional generation of 3D brain tumor regions via VQGAN and temporal-agnostic masked transformer
- Canadian Association of Radiologists JournalDevelopment and evaluation of an automated protocol recommendation system for chest CT using natural language processing with CLEVER terminology word replacement
- Proceedings of IEEE International Conference on Bioinformatics and BiomedicineEdge-enhanced dilated residual attention network for multimodal medical image fusion
- Proceedings of the International Society for Optics and Photonics, SPIE Medical ImagingEnhancing MR-guided laser interstitial thermal therapy planning using U-Net: a data-driven approach for predicting MR thermometry images
202310 papers
- Canadian Association of Radiologists JournalAutomated adolescence scoliosis detection using Augmented U-Net with non-square kernels (opens in a new tab)
- International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) WorkshopsCombining weakly supervised segmentation with multitask learning for improved 3D MRI brain tumour classification (opens in a new tab)
- Canadian Association of Radiologists JournalEnd-to-end pediatric low-grade glioma segmentation and classification (opens in a new tab)
- IEEE AccessEvaluating knowledge transfer in neural network for medical images (opens in a new tab)
- Frontiers in Public Health, section Digital Public HealthExploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning (opens in a new tab)
- Clinical RadiologyHepatocellular adenoma subtyping by qualitative magnetic resonance imaging features and hepatocellular adenoma subtyping by qualitative MRI features and machine learning algorithm of integrated qualitative and quantitative features: a proof-of-concept study (opens in a new tab)
- International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) WorkshopsMultimodal learning for improving performance and explainability of chest X-ray classification (opens in a new tab)
- American Journal of NeuroradiologyT2-FLAIR mismatch sign in pediatric low-grade glioma (opens in a new tab)
- Using large text to image models with structured prompts for skin disease identification. International Conference on Computer Vision (ICCV) WorkshopsU, Castro, R., Khalvati, F., Aich, S (opens in a new tab)
- ACS Applied Nano MaterialsUsing machine learning and silver nanoparticle-based surface-enhanced Raman spectroscopy for classification of cardiovascular disease biomarkers (opens in a new tab)
Where alumni went47 listed
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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