Jeya Maria Jose

Hi!, I am a 3rd year Ph.D. student at Johns Hopkins University, in the Department of Electrical and Computer Engineering where I am working in Vision and Image Understanding Lab , advised by Dr. Vishal M Patel .

My research focus lies within the intersection of Computer Vision, Machine Learning, and Medical Imaging. More specifically, I work on image/3D segmentation, image enhancement, image-to-image translation for large-scale vision and medical imaging tasks.

I interned at Adobe in the summer of 2021 where I worked on image harmonization problems. During my undergrad days, I worked at the Medical Mechatronics Lab in National University of Singapore on medical image segmentation and survival prediction problems. I graduated from NIT Trichy, India in 2019 with my Bachelor's degree majoring in Instrumentation and Control.

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News

  • June, 2021 - 2 papers accepted at MICCAI 2021 .
  • May, 2021 - Joined Adobe as a Research Intern.
  • May, 2021 - 1 paper accepted at ICIP 2021 .
  • November, 2020 - 1 paper accepted in IEEE Journal of Selected Topics in Signal Processing .
  • November, 2020 - 1 paper accepted at WACV 2021 .
  • July, 2020 - Recipient of MICCAI Student Travel Award for the year 2020.
  • May, 2020 - 1 paper accepted in the IEEE Journal of Selected Topics in Signal Processing .
  • May, 2020 - 1 paper accepted at MICCAI 2020 (Early Acceptance).
  • September, 2019 - Awarded Best Student Paper award at CVIP 2019 .
  • August, 2019 - Joined Johns Hopkins University for my Ph.D with ECE fellowship.



Teaching

Teaching Assistant: Deep Learning EN.520.638.01.SP21, Spring 2021, Johns Hopkins University

Research

R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection
arXiv Preprint 2021

Jeya Maria Jose, Christina Chen, and Vishal M. Patel

Paper

SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
arXiv Preprint (under review at ICRA 2022)

Chaminda Bandara, Jeya Maria Jose, and Vishal M. Patel

Paper | Code

Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
MICCAI 2021

Jeya Maria Jose, Poojan Oza, Ilker Hacihaliloglu, Vishal M. Patel

Paper | Code

Over-and-Under Complete Convolutional RNN for MRI Reconstruction
MICCAI 2021

Pengfei Guo, Jeya Maria Jose, Puyang Wang, Jinyuan Zhou,Shanshan Jiang, Vishal M. Patel

Paper | Code

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
Preprint, Under Review at IEEE TMI


Jeya Maria Jose, Vishwanath Sindagi, Ilker Hacihaliloglu, Vishal M. Patel

Paper | Code | Project

Overcomplete Deep Subspace Clustering Networks
WACV 2021

Jeya Maria Jose and Vishal M. Patel

Paper | Code

Overcomplete Representations against Adversarial Videos
ICIP 2021

Shao-Yuan Lo, Jeya Maria Jose, and Vishal M. Patel

Paper

Exploring Overcomplete Representations for Single Image Deraining using CNNs
IEEE Journal of Selected Topics in Signal Processing

Rajeev Yasarla*, Jeya Maria Jose*, Vishal M. Patel

*equal contribution

Paper | Code

KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations
MICCAI 2020

Jeya Maria Jose, Vishwanath Sindagi, Ilker Hacihaliloglu, Vishal M. Patel

Paper | Code | Project

Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data
IEEE Journal of Selected Topics in Signal Processing

Jeya Maria Jose, Rajeev Yasarla, Puyang Wang, Ilker Hacihaliloglu, Vishal M. Patel

Paper

Tackling Multiple Visual Artifacts: Blind Image Restoration using Conditional Adversarial Networks
CVIP 2019 (Best Student Paper Award)

M Anand, A Ashwin Natraj, Jeya Maria Jose, K Subramanian, S Deivalakshmi

Paper | Code

Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
BraTS, MICCAI Workshop 2019

Mobarakol Islam, Vibashan VS, Jeya Maria Jose, Navodini Wijethilake, Uppal Utkarsh, Hongliang Ren

Paper

BP-Net: Cuff-less Blood Pressure Prediction using Convolutional and Long Short-term Memory Networks from ECG and PPG signals

Jeya Maria Jose, M Anand, Geerthy T, M Siddarth, K Subramanian, G Uma

Paper (Feature Analysis using ML) | Code | Paper (BP-Net) - Coming Soon

Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information
BraTS, MICCAI Workshop 2018

Mobarakol Islam, Jeya Maria Jose, Hongliang Ren

Paper | Poster

Services

Web Chair: AVSS 2021

Reviewer at:

Journals : IEEE - TMI, TCSVT; Springer - IJCV; Elsevier - PR, CBM
Conferences : CVPR, ICCV, ICLR, MICCAI, WACV, ICIP
Workshops : BrainLes, Medical Image Learning with Less Labels and Imperfect Data

source code