Researcher Phd Candidate @ Philips AI Research Hub France In collaboration with CREATIS Lab and HCL Lyon

Publications :

Weakly-Supervised learning based Pathology Detection and Localization in 3D Chest Scans
A. Djahnine, E. Jupin-delevaux, O. Nempont, F. Craighero, S. A. Si-Mohamed, V. Cottin, P. Douek, A. Popoff, L. Boussel
The International Journal of Medical Physics Research and Practice (Medical Physics)In revision

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution
A. Djahnine, C. Lazarus, M. Lederlin, S. Mulé, R. Wiemker, S. Si-Mohamed, E. Jupin-Delevaux, O. Nempont, Y. Skandarani, M. De Craene, S. Goubalan, C. Raynaud, Y. Belkouchi, A. Ben Afia, C. Fabre, G. Ferretti, C. De Margerie, P. Berge, R. Liberge, N. Elbaz, M. Blain, P. Brillet, G. Chassagnon, F. Cadour, C. Caramella, M. El Hajjam, S. Boussouar, J. Hadchiti, X. Fablet, A. Khalil, H. Talbot, A. Luciani, N. Lassau and L. Boussel
Diagnostic and Interventional Imaging Journal (DII)

BERT-based natural language processing analysis of French CT reports: Application to the measurement of the positivity rate for pulmonary embolism
E. Jupin-Delevaux, A. Djahnine, F. Talbot, A. Richard, S. Gouttard, A. Mansuy, P. Douek, S. A. Si-Mohamed, L. Boussel
Research in Diagnostic and Interventional Imaging Journal (ReDII)

Automatization and development of the TAVI CAPRI risk score with imaging biomarkers
P.-J. Lartaud, B. Harbaoui, A. Djahnine, O. Nempont, J.-M. Rouet, Anna Vlachomitrou, B. Riche, M. Rabilloud, S. Si-Mohamed P. Douek, F. Derimay, G. Rioufol, P. Lantelme, L. Boussel
Radiology: Artificial IntelligenceIn revision

Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT Scans
Djahnine Aissam, Popoff Alexandre, Jupin-Delevaux Emilien, Cottin Vincent, Nempont Olivier, Boussel Loic
Arxiv

Conferences & Summer Schools :

16 th IEEE International Conference on Signal Processing (ICSP)
21 - 24 October 2022
Beijing, China
Tailored 3D CT contrastive pretraining to improve pulmonary pathology classification
A. Djahnine, A. Popoff, E. Jupin-Delevaux, V. Cottin, O. Nempont, L. Boussel

Oxford Machine Learning Summer School (OxML 2023)(ML x Health Track, In Person) Certificate of Participation
13 - 16 July 2023
Oxford, United Kingdom

Honors & awards

JFR 2023 Data Challenge WINNER : Pancreatic masses detection in 3D CT scans

• I was a member of the Philips team in collaboration with Hospices Civils de Lyon that won the JFR (les Journées Francophones de Radiologie) data challenge. The solution used deep learning-based algorithm to detect pancreatic masses in 3D CT scans.
  

JFR 2022 Data Challenge WINNER : Pulmonary embolism detection in 3D CT scans

• I was a member of the Philips team in collaboration with Hospices Civils de Lyon that won the JFR (les Journées Francophones de Radiologie) data challenge. The solution used Deep Learning for Computer Vision to detect pulmonary embolism in CT scans. ( Challenge Paper, Our Solution (Journal Paper))

Academic Services

Reviewer : 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

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Computer Vision and Machine Learning Msc Graduate at Sorbonne Université (2018-2020)

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Research Projects

Computer Vision and Deep Learning Intern at GE Healthcare

Investigation of automatic search methods for neural network hyperparameters (Neural Architecture Search). Application to Mammographic Data within WHARe team (Women’s Health Applied Research)

• In this internship, we focused on improving mammography systems for clinical diagnosis by utilizing deep learning techniques. We specifically explored the efficiency of a gradient-based NAS method called DARTS (Differentiable Architecture Search) for classification tasks on mammographic data. Through experiments, we achieved state-of-the-art results on mammography data classification, outperforming existing models.

Unsupervised Spatiotemporal Data Inpainting

• This a PyTorch implementaiton of this paper : Unsupervised Spatiotemporal Data Inpainting Under review at ICLR 2020.
• The code is implemented in collaboration of : Ilyas aroui Github, Raouf Keskes Github
• Inpainting spatio-temporal sequences is an active research topic that relies heavily on supervision with large datasets. In this work, we consider the problem of reconstructing missing information with an unsupervised learning approach. Following the work of Kim et al. We train a generative model on the occluded sequences. We ensure that the models captured both frame-based and sequential based information. Our proposed model is adapted to large-scale images and can be used to different types of sequences and occlusion processes.

Cell Images Segmentation Using Cycle Generative Adversarial Network

• During our 6-month curriculum project with Institut Pasteur and Sorbonne University, we focused on cell image segmentation for various medical applications. Recognizing the significance of labeled data for training accurate convolutional neural networks (CNNs), we adopted a cycleGAN framework to overcome the challenge of limited data. Our research objectives were two-fold: first, to generate synthetic cell images that mimic the distribution of input images for data augmentation, combining them with real cell images to train a context-aware CNN for precise cell segmentation. Second, we proposed a segmentation method based on cycle-consistent generative adversarial networks (CycleGANs), which allowed us to train the model even in the absence of prepared image-mask pairs.

Histopathological Images Generation Using Generative Adversarial Network

• Here, we implement an approach to histopathological image generation that overcomes the challenge of dataset size (small datasets) in medical field by utilizing a GAN framework (Generative adversarial network). The main objective of this research project is :
• Generate synthetic histopathological images that model the distribution of the input images for data augmentation. Use both of the synthetic and real images for training in different tasks (detection/segmentation/tracking).
  

Online Courses

• Coursera : Deep learning Specialization
• Udemy :
  • Python for Data Science and Machine Learning Bootcamp Certificate.
  • Complete Amazon AWS Certified Solution Architect Course