Deep learning with weak or few labels in medical image analysis
Thèmes scientifiques :
- B - Image et Vision
- T - Apprentissage pour l'analyse du signal et des images
Nous vous rappelons que, afin de garantir l'accès de tous les inscrits aux salles de réunion, l'inscription aux réunions est gratuite mais obligatoire.
62 personnes membres du GdR ISIS, et 25 personnes non membres du GdR, sont inscrits à cette réunion.
Capacité de la salle : 200 personnes.
In recent years, artificial intelligence, especially deep learning, has received a lot of attention to explore and structure multidimensional and multimodality medical imaging data for a wide variety of tasks ranging from low to high level image processing and analysis such as segmentation, image synthesis, diagnosis and prognosis models characterizing pathological patterns in the data or predicting the course and outcome of diseases, respectively, as well as therapy monitoring. The amount of high-quality annotated data is critical for the training of deep learning networks; however in the medical field, annotations are known to be costly to acquire. Hence, many approaches have been set up to address the lack of labels, the small quantity of labels, noisy labels, based on training strategies, novel architectures or data generation etc.
This one-day workshop intends to gather researchers in deep machine learning, computer vision and/or medical image analysis as well as companies and AI-based startups in the medical image field, interested on how to mitigate the need for large amounts of annotated data in deep learning, for various medical image analysis tasks: image segmentation, registration, detection, image super-resolution, image synthesis, etc. We welcome contributions addressing, but not limited to, the following topics:
- weakly supervised learning, self-supervised learning, semi-supervised learning, one-shot learning
- transfer learning, domain adaptation, regularized training
- data augmentation, synthetic data generation
- attention based architectures, including transformers
- Hervé Delingette DR INRIA Sophia- Antipolis, Asclepios Team
- Hoel Kervadec Research Fellow - Erasmus MC Rotterdam
Call for participation
The day will include short presentations (20-30 min including questions). Interested participants should send the organizers an abstract (1/2 page) that includes the authors names and affiliations. Students are particularly encouraged to participate. Deadline for application is : 19-01-2022
- Carole Lartizien(CREATIS Lyon), firstname.lastname@example.org
- Caroline Petitjean (LITIS Rouen), email@example.com
- Nicolas Thome (CNAM Paris), firstname.lastname@example.org
- Mireille Garreau (LTSI Rennes), email@example.com
CNRS Délégation Ile-de-France Villejuif - 7 Rue Guy Môquet, 94800 Villejuif
Résumés des contributions
Title : Some Strategies to cope with the cost of annotations in Medical Image Analysis.
Hervé Delingette - INRIA Sophia- Antipolis
Image annotations such as image labels or organ delineations are required to train supervised learning algorithms to solve various tasks in medical image analysis but also to evaluate their performance. Producing high quality annotations is very time consuming especially when dealing with volumetric images. Furthermore, inter-rater variability when producing those annotations has to be taken into account to reflect the complexity of the tasks. In this lecture, I will present some strategies related to data and models to cope with the cost of annotations. A first set of approaches are data-centric and aim to keep only high quality annotations and to precisely measure the agreement or disagreement between the raters. A second set of methods focused on machine learning models try to minimize the amount of required strong annotations for instance through the use of semi-supervised or mixed-supervised techniques.
Title : Beyond pixel-wise supervision: semantic segmentation with few shape descriptors
Hoel Kervadec - Erasmus MC Rotterdam
In the context of image semantic segmentation, neural networks are most often supervised with variants of standard losses such as cross-entropy or dice. While effective?as showed by the considerable progresses made over the past few years?this remains at its core pixel-wise classification, discarding the spatial and geometrical information altogether. We could say that those losses are ?micro-managing? each and every pixel, instead of supervising the ?big-picture?: where the predicted object is, does it have the desired shape? There exists an extensive literature in pre-deep-learning computer vision to describe and characterize objects; a few descriptors can be sufficient to reconstruct complex shapes. It is already known that some can be used as regularizer while training deep-neural networks, to improve smoothness or remove spurious pixels for instance. But, as far as we are aware of, it has never been shown if shape descriptors could be powerful enough to supervise a neural network wholly on their own, without resorting to any pixel-wise supervision.
Not only interesting theoretically, there exist deeper motivations to pose segmentation problems as a reconstruction of shape descriptors: First, annotations to obtain approximations of low-order shape moments could be much less cumbersome than their full-mask counterparts, and anatomical priors could be readily encoded into invariant shape descriptions, which might alleviate the annotation burden. Finally, and most importantly, we hypothesize that, given a task, certain shape descriptions might be invariant across image acquisition protocols/modalities and subject populations, which might open interesting research avenues for generalization in medical image segmentation. This talk will first present recent works in constrained deep neural networks, that enable the use of general shape descriptors as supervision methods. Then, we introduce and re-formulate a few shape descriptors in the context of image segmentation, and evaluate their potential as stand-alone losses on two different, challenging tasks. Very surprisingly, as little as 4 descriptors per class can approach the performance of a segmentation mask with 65k individual discrete labels. We also found that shape descriptors can be a valid way to encode anatomical priors about the task, leveraing expert knowledge without additional annotations.