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Stage M2 | PFE : Deep characterization of prostate cancer in multi-parametric imaging

13 December 2023


Catégorie : Stagiaire


Internship at IMT Atlantique - LaTIM UMR 1101, Inserm in Brest

M2 - Last year engineering student

Deep characterization of prostate cancer in multi-parametric imaging

 

IMT Atlantique - LaTIM UMR 1101, Inserm is looking for a M2 student motivated by image analysis with a particular interest in deep learning applications.

Title: Deep characterization of prostate cancer in multi-parametric imaging

Context: With over one million new diagnoses and 350,000 deaths worldwide each year, Prostate Cancer (PCa) is the second most common cancer in men. Recommended before biopsies, multi-parametric Magnetic Resonance Imaging (mp-MRI), which consists in acquiring multiple dedicated diffusion and anatomical MRI sequences, is playing an increasing role in early diagnosis. However, visual analysis of prostatic mp-MRI data requires considerable expertise due to the heterogeneity of pathological patterns, and suffers from poor inter-reader agreement, sub-optimal interpretation and over-diagnosis. Furthermore, it does not allow to determine the cancer aggressiveness, as characterized by the Gleason Score (GS). In a context where active surveillance is recommended when a low-aggressivity cancer has been identified, it is crucial to accurately characterize cancer progression using non-invasive methods. For all these reasons, research into Deep Learning (DL) based diagnosis models to help radiologists analyze mp-MRI has been prolific in recent years. To improve the performance of these models and move towards the clinical transfer of such tools, we propose to focus on multi-modal information fusion for detecting/segmenting prostate lesions and characterizing their aggressiveness, major challenges as differentiation between low- and medium/high-grade PCa is an important clinical determinant.

Internship topic: In this context, we are interested in the development of multi-task networks that can simultaneously detect/segment lesions and characterize their clinical stage. The main methodological objective of the internship will be to take advantage of redundancies and complementarities between channels by considering four mp-MRI sequences (T2, ADC, B2000, perfusion), thereby extending bimodal fusion approaches previously proposed for PCa imaging that used only T2 and ADC. A priority will be given to the development of innovative DL architectures that can leverage the various modalities through cross-modality learning. Cross-modality learning is usually performed with architectures containing many layers specific to each modality, which does not allow to fully exploit potentially valuable inter-modal information. Efforts will be devoted to the design of more compact models by widely re-using network parameters (e.g., sharing convolution kernels between modalities). Through cross-modality learning, the contributions will aim at obtaining more accurate and synthetic models, with associated uncertainty quantification. Multi-modal medical Transformers models for segmentation and detection will be primarily considered, in line with prior research conducted at LaTIM.

Candidate profile: We are looking for a M2 student motivated by image analysis with a particular interest in DL applications. A background in biomedical or medical imaging and an experience with Python programming language and Pytorch package are a plus. Good communication and team working skills are also required as the intern will work in close collaboration with another team from INSA Lyon / CREATIS laboratory located in Lyon, France. A good ability to communicate in English as well as a fluent English for reading and writing scientific articles are also required.

Internship information:

  • 6-month internship starting from February to April 2024
  • Location : IMT Atlantique, Technopôle Brest-Iroise, Brest, France
  • Advisors: P.-H. Conze (IMT Atlantique, LaTIM) and V. Jaouen (IMT Atlantique, LaTIM)
  • Applications by mail to pierre-henri.conze@imt-atlantique.fr and vincent.jaouen@imt-atlantique.fr including CV, cover letter stating your motivation and fit for this project, latest grade transcripts and recommendation letters or contacts from former teachers/advisors