Context

An accurate diagnosis of cancer often requires a histological examination, during which histopathologists analyze tissue samples at microscopic level. They aim not only at detecting cancer but also characterizing it. Cells’ size and shape, biomarkers, along with other elements are studied and contribute to defining the cancer type and grade. From this diagnosis, oncologists will decide on a treatment.

In histology, obtaining quality annotation is an expensive and lengthy process as it requires expert knowledge. Additionally, even for experts, giving a cell level precision on annotations is almost impossible and often subjective. Therefore, we speculate that being able to have slide level annotations or taking into account the annotation imprecision in our learning tasks could greatly reduce the need for annotation and improve models accuracy in lesion segmentation tasks. As a matter of fact, many authors already leverage the power of weakly supervised learning for histopathology [1, 2, 3].

During this internship, we will apply weakly supervised learning methods to the previously mentioned challenges. More precisely, the intern will perform:

• lesion segmentation based on slide level labels
• lesion segmentation based on partial and imprecise annotations

[1] HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images, Chan_2019
[2] A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains, Chan, 2020
[3] Weakly supervised multiple instance learning histopathological tumor segmentation, Le Rousseau, 2021

Internship objectives

• Perform an extensive literature review of the weakly supervised learning techniques usually used
• Select relevant approaches for histology and more specifically the applications at hand
• Implement and compare such approaches for each of the above applications

Practical information

• Remuneration : 1000€/month + 50% navigo refund
• Primaa is based in Paris, 2ème arrondissement
• possible occasional remote

Applied mathematics student.

• Six months internship (start between march and May 2023.)
• Knowledge in python.
• Knowledge in computer vision (image processing, deep learning) : tensorflow and/or pytorch, scikit-image….

Meeting with two data team members