Computer Vision in Medical Imaging (CVMI) Research
Current areas of research
1. Thoracic disease detection from a Chest X-ray image
The main purpose of this work is detecting thoracic disease from a Chest X-ray image and multiple disease from one image, if present. We are currently working on visualizing the anomaly in parallel to disease detection which can further help the radiologist to better analyze and make concluding remarks.
2. Missed or delayed fracture detection from X-rays
We are currently working on the missed or delayed fracture detection to draw the attention of doctors to the affected area using deep learning. Also, we are working on the localization of the fracture to pinpoint the exact location of the fracture which can help the radiologist in accurate detection.
3. Automatic segmentation and annotation of medical images
Automatic segmentation and annotation of medical image plays a critical role. Automatic segmentation and annotation not only increase the efficiency of clinical workflow, but also prevent overburdening of radiologists. The objective of this work is to improve the accuracy and give probabilistic map for annotation.
4. Early Sepsis prediction from ICU Patient’s data
The main purpose of the project is to predict sepsis 6 hours earlier from patient’s physiological data. After getting the cleaned data we are using ML method to see their performance, but the accuracy is affected by class imbalance problem on which we are currently working.
Fine-grained visual classification (FGVC) is challenging but more critical than traditional classification tasks. It requires distinguishing different subcategories with the inherently subtle intra-class object variations. Previous works focus on enhancing the feature representation ability using multiple granularities and discriminative regions based on the attention strategy or bounding boxes. However, these methods highly rely on deep neural networks which lack interpretability. Our target is to implement on medical images using some attention guided framework which can guide the network to extract discriminative regions in an interpretable way, a progressive training mechanism as the complexity of the problem, and transfer learning based on pretrained model on medical images.
6. Multimodality Breast Cancer Detection Using Artificial Intelligence
Many imaging modalities such as ultrasound, (X-ray) mammography, breast MRI are frequently used to examine the Breast Cancer severity. All imaging modalities have their pros and cons depending upon cost, image resolution, radiation uses etc. Further, early detection and disease severity prediction is a major challenge in medical practices. Previous methods use machine learning based methods for classification of breast cancer into corresponding BI-RADS (Breast Imaging Reporting and Database system) scores. In the proposed work we intend to use deep learning-based methods for completely automated solution of breast cancer detection and disease severity classification. Further, based on the BI-RADS guidelines we suggest the patient for a biopsy of the diseased area. Once the biopsy images of the same patient are available, we’ll find the correlation between the histopathological images and corresponding radiological images. Finally, we’ll validate our system using some performance analysis and statistical tools.
Komal Kumar, Snehashis Chakraborty, Sudipta Roy*, “Self-Supervised Diffusion Model for Anomaly Segmentation in Medical Imaging,” 10th International Conference on Pattern Recognition and Machine Intelligence (PReMI2023), Proceedings to be published by prestigious Springer LNCS series, December 12 - 15, 2023, ISI Kolkata, India [Accepted ]
Komal Kumar, Balakrishna Pailla, Kalyan Tadepalli, Sudipta Roy*, "Robust MSFM Learning Network for Classification and Weakly Supervised Localization," 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), CVAMD, Paris, France, Time: October 2, 2023 [Accepted]
Debojyoti Pal, Tanushree Meena, Dwarikanath Mahapatra, Sudipta Roy*, "AW-Net: A Novel Fully Connected Attention-based Medical Image Segmentation Model," 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), CVAMD, Paris, France, Time: October 2, 2023 [Accepted ]
Dwarikanath Mahapatra*, Behzad Bozorgtabar, Sudipta Roy, Zongyuan Ge, Mauricio Reyes, “Multi-Label Active Learning for CXR Classification Using Graph Attention Transformers and Data Augmentation,” Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2023, Springer LNCS, Oct 8-12, 2023, Vancouver, Canada. [Accepted ]
Dwarikanath Mahapatra, Antonio Jose Jimeno Yepes, Shiba Kuanar, Sudipta Roy, Behzad Bozorgtabar, Mauricio Reyes, Zongyuan Ge, “Class Specific Feature Disentanglement for Multi-Label Generalized Zero Shot Medical Image Classification,” Medical Image Computing and Computer-Assisted Intervention 2023, MICCAI, Springer LNCS, Oct 8-12, 2023, Vancouver, Canada. [Accepted ]
Debojyoti Pal, Tanushree Meena, Sudipta Roy*, “A Fully Connected Generalized SE-UResNet for Multiorgan Chest Radiographs Segmentation,” IEEE 24th International Conference on Information Reuse and Integration for Data Science (IEEE IRI), August 4 - August 6, 2023, Seattle, WA, USA. [Accepted ]
Tanushree Meena, Anwesh Kabiraj, Balakrishna Reddy Pailla, Sudipta Roy*, “Weakly Supervised Confidence Aware Probabilistic CAM multi-Thorax Anomaly Localization Network,” IEEE 24th International Conference on Information Reuse and Integration for Data Science (IEEE IRI), August 4 - August 6, 2023, Seattle, WA, USA. [Accepted ]
Snehashis Chakraborty, Komal Kumar, Balakrishna Reddy Pailla, Sudipta Roy*, “An Explanable AI based clinical assistance model for identifying patients with the onset of sepsis,” IEEE 24th International Conference on Information Reuse and Integration for Data Science(IEEE IRI), August 4 - August 6, 2023, Seattle, WA, USA. [Accepted ]
Varun Srivastava, Deepika Kumar and Sudipta Roy*, "A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer," Computer and Electrical Engineering, ELSEVIER, Vol 102, September 2022, 108259. https://doi.org/10.1016/j.compeleceng.2022.108259.
Associate Professor, Artificial Intelligence & Data Science, Jio Institute, India
Researchers
Mr. Debojyoti Pal
Research Assistant, CVMI, Jio Institute
Mr. Komal Kumar
Research Assistant, CVMI, Jio Institute
Mr. Pankaj K. Jain
Research Associate, CVMI, Jio Institute
Mr. Snehasish Chakroborty
Research Assistant, CVMI, Jio Institute
Ms. Tanushree Meena
Research Assistant, CVMI, Jio Institute
Team Associates
Mr. Balakrishna Reddy
Vice President at Reliance AI-CoE
Dr. Kalyan Tadepalli
Student Mentor, Artificial Intelligence & Data Science Programme, Jio Institute | Consultant, AI Centre of Excellence, Reliance Jio, India
Highlights
High performance computes for Research lab
A network of High Performance Computing services equipped with state-of-the-art computing and graphic processing power. Out system include Intel i9 processors, NVIDIA dedicated GPUs, and large RAM and storage for research purposes.
AR/VR Lab for research
A network of High Performance Computing services equipped with state-of-the-art computing and graphic processing power. Out system include Intel i9 processors, NVIDIA dedicated GPUs, and large RAM and storage for research purposes.