Ashwin Raju
I am a Machine learning enthusiast who has 7 years of experience in applying computer vision, machine learning alogrithms to solve problems that directly impact people. sI have developed and patented algorithms which solves real world problems in medical field. I have expertise in applying machine learning models to diverse areas such as laparoscopic surgery, radiology, histology.
Work Experience
Research Intern
Designed a statistical shape model that can be incorporated with the deep learning framework to estimate rigid and non-rigid pose estimations. Our designed statistical shape model learns the prior anatomical shape and significantly reduces the variance of multi-organs. Tested on Liver and Larynx organs.
- Published at AAAI'22 with Oral acceptance.
Research Intern
Designed a framework to integrate co-training, hetero-modality and adversarial learning to segment multi-organs in a semi-supervised approach. Improved the pathological liver mask dice coefficients by ranges of 4.2% to 9.4%.
- Published at MICCAI'20.
Research Intern
Designed a framework to incorporate minimal-labor user interactions to segment multi-organs. Improved the pathological liver mask dice coefficients by 3% when compared to the state of the art models.
- Published at ECCV'20.
Graduate Research Assistant
- Designed a framework to classify Cancer stages from histology images by incorporating Attention multi-instance learning in a form of a graph.
- Implemented several Deep learning based algorithms using tensorflow as a proof of concept.
- Designed a framework to classify Cancer stages from histology images by incorporating Attention multi-instance learning in a form of a graph.
Projects
Cancer cell localization
Worked on a project which involves localization of 51 different cancer cells present in a gold standard dataset. Working on building a novel Semi-Supervised model keeping Feature Pyramid Network, Attention Model as baseline.
Nuclei cell segmentation
Implemented several state-of-the-art models such as Mask-RCNN, UNet, SegNet, DeepLab v2 to understand the architecture and evaluate the performance of these models for Nuclei Cell Segmentation. Experimented the state-of-the-art techniques with several public datasets.
Multi-class artefact detection in video endoscopy
Classification of 7 classes in Endoscopic videos. The model is built on ensembling VGGNet and GoogLeNet to produce high performance results. The network achieves a Mean Average Precision of 63.8% in test dataset and won first place in M2CAI challenge.
Publications
- Ashwin Raju, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, Adam P. Harrison. DISSM: Deep Implicit Statistical Shape Models for 3D Medical Image Delineation, Association for the Advancement of Artificial Intelligence (AAAI 2022) [ORAL] (Acceptance Rate: 15%)
- Ashwin Raju, Chi-Tung Cheng, Yuankai Huo, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHung Liao, Adam P. Harrison. CHASe: Co-heterogeneous and Adaptive Segmentation from Multi-source and Multi-phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation, European Conference on Computer Vision (ECCV 2020) (Acceptance Rate: 25%)
- Ashwin Raju, Jiawen Yao, Mohammad MinHazul Haq, Jitendra Jonnagaddala, Junzhou Huang. Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging, Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) (Acceptance Rate: 25%)
- Ashwin Raju, Zhanghexuan Ji, ChiTung Cheng, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, Chien Hung Liao, Adam P. Harrison. User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation, Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) (Acceptance Rate: 25%)
- Fengze Liu, Jin-zheng Cai, Yuankai Huo, Chi-Tung Cheng, Ashwin Raju, Dakai Jin, Jing Xiao, Alan Yuille, Le Lu, ChienHung Liao, and Adam P. Harrison. JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-modal Image Alignment of Large-Scale Pathological CT Scans, European Conference on Computer Vision (ECCV 2020) (Acceptance Rate: 25%)
- Ram Srivatsa Kannan, Lavanya Subramanian, Ashwin Raju, Jeongseob Ahn, Jason Mars, Lingjia Tang. GrandSLAm: Guaranteeing SLAs for Jobs in Microservices Execution Frameworks, European Conference on Computer Systems (Eurosys-19)
- Sheng wang, Ashwin Raju, Junzhou Huang. Deep learning based multi-label classification for surgical tool presence detection in laparoscopic videos, IEEE Inter- national Symposium on Biomedical Imaging (ISBI 2017)
Interests
Travelling, Binge watching