Utilisation de l'auto-apprentissage pour réduire le coût d'annotation pour la segmentation d'image en pathology digitale

peer reviewedData scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based approach that can exploit both (few) exhaustively annotated images and (very) sparsely-annotated images to improve the training of deep learning models for image segmentation tasks. The approach is evaluated on three public and one in-house datasets, representing a diverse set of segmentation tasks in digital pathology. The experimental results show that self-training allows to bring significant model improvement by incorporating sparsely annotated images and proves to be a good strategy to relieve labeling effort in the digital pathology domain

Aborder la pénurie de donnée avec des techniques d'apprentissage profond par transfert et auto-apprentissage en pathologie digitale

Pathology, the field of medicine and biology interested in studying and diagnosing diseases, is on the brink of a revolution with technological advances in artificial intelligence and machine learning. Traditionally, in this field, the medium which has been used for research and diagnosis is a glass slide on which tissue and cell samples are applied and later analyzed under an optical microscope. Dedicated scanners are nowadays able to digitize these glass slides into large digital images called whole-slide-images which can then be reviewed on a computer. This new medium also offers unprecedented opportunities for computers to assist practitioners by automating the most time-consuming and tedious analysis tasks. The field which is interested in these digitization, automation and related topics is called digital pathology. Machine and deep learning methods are great candidates for tackling these automation tasks thanks to their ability to automatically learn models and capture complex patterns directly from data. However, digital pathology presents several challenges for learning methods. In particular, the field is suffering from data scarcity as data, especially annotated, is difficult to obtain because of privacy concerns, cost of annotations, etc. In this thesis, we explore different machine learning techniques tailored for tackling data scarcity. We first study different deep transfer learning techniques, a family of methods which consist in re-using a model that has been learned on a different task than the target task. We investigate best practices regarding how deep \acrlong{cnn} models pre-trained on ImageNet, a dataset of photographs, can be transferred to digital pathology image classification tasks. We notably show that, in digital pathology, fine-tuning outperforms feature extraction and draw other practical conclusions regarding transfer from ImageNet. Motivated by the fact that transfer performs better when the source and target tasks are close, we then use multi-task learning to pre-train a model on pathology data directly. We show that this technique is efficient for creating a transferrable model tailored for pathology tasks. Finally, we move to the topic of self-training, a family of methods where a model being learned is used to annotate unlabeled data that is then incorporated into the training process. In particular, we apply this technique to image segmentation for exploiting a dataset which has been only sparsely-labeled. We show that our approach is able to make use of the sparsely-labeled data better than a supervised approach

SLDC: an open-source workflow for object detection in multi-gigapixel images

CYTOMINE, Histowe

Comparaison de stratégies de transfert profond pour la pathologie digitale

peer reviewedIn this paper, we study deep transfer learning as a way of overcoming object recognition challenges encountered in the field of digital pathology. Through several experiments, we investigate various uses of pre-trained neural network architectures and different combination schemes with random forests for feature selection. Our experiments on eight classification datasets show that densely connected and residual networks consistently yield best performances across strategies. It also appears that network fine-tuning and using inner layers features are the best performing strategies, with the former yielding slightly superior results

Pré-entraînement multi-tâche de réseaux de neurones pour la pathologie digitale

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance

Relieving pixel-wise labeling effort for pathology image segmentation with self-training

Data scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based approach that can exploit both (few) exhaustively annotated images and (very) sparsely-annotated images to improve the training of deep learning models for image segmentation tasks. The approach is evaluated on three public and one in-house dataset, representing a diverse set of segmentation tasks in digital pathology. The experimental results show that self-training allows to bring significant model improvement by incorporating sparsely annotated images and proves to be a good strategy to relieve labeling effort in the digital pathology domain

BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows

Peer reviewe

BIAFLOWS: A collaborative framework to benchmark bioimage analysis workflows

Automated image analysis has become key to extract quantitative information from microscopy images, however the methods involved are now often so complex that they can no longer be unambiguously described using written protocols. We introduce BIAFLOWS, a web based framework to encapsulate, reproducibly deploy, and benchmark automated bioimage analysis workflows. BIAFLOWS helps diffusing and fairly comparing image analysis methods, hence safeguarding research based on their results by enforcing highest quality standards. All rights reserved. No reuse allowed without permission. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the praeprint in perpetuity