118 research outputs found
Learning from crowds in digital pathology using scalable variational Gaussian processes
This work was supported by the Agencia Estatal de Investigacion of the Spanish Ministerio de Ciencia e Innovacion under contract PID2019-105142RB-C22/AEI/10.13039/501100011033, and the United States National Institutes of Health National Cancer Institute Grants U01CA220401 and U24CA19436201. P.M. contribution was mostly before joining Microsoft Research, when he was supported by La Caixa Banking Foundation (ID 100010434, Barcelona, Spain) through La Caixa Fellowship for Doctoral Studies LCF/BQ/ES17/11600011.The volume of labeled data is often the primary determinant of success in developing machine
learning algorithms. This has increased interest in methods for leveraging crowds to scale data
labeling efforts, and methods to learn from noisy crowd-sourced labels. The need to scale labeling is
acute but particularly challenging in medical applications like pathology, due to the expertise required
to generate quality labels and the limited availability of qualified experts. In this paper we investigate
the application of Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR) in digital
pathology. We compare SVGPCR with other crowdsourcing methods using a large multi-rater dataset
where pathologists, pathology residents, and medical students annotated tissue regions breast
cancer. Our study shows that SVGPCR is competitive with equivalent methods trained using goldstandard
pathologist generated labels, and that SVGPCR meets or exceeds the performance of other
crowdsourcing methods based on deep learning. We also show how SVGPCR can effectively learn
the class-conditional reliabilities of individual annotators and demonstrate that Gaussian-process
classifiers have comparable performance to similar deep learning methods. These results suggest
that SVGPCR can meaningfully engage non-experts in pathology labeling tasks, and that the classconditional
reliabilities estimated by SVGPCR may assist in matching annotators to tasks where they
perform well.Agencia Estatal de Investigacion of the Spanish Ministerio de Ciencia e Innovacion PID2019-105142RB-C22/AEI/10.13039/501100011033United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) U01CA220401
U24CA19436201La Caixa Banking Foundation (Barcelona, Spain) Barcelona, Spain) through La Caixa Fellowship 100010434
LCF/BQ/ES17/1160001
The U-Net-based Active Learning Framework for Enhancing Cancer Immunotherapy
Breast cancer is the most common cancer in the world. According to the U.S. Breast Cancer Statistics, about 281,000 new cases of invasive breast cancer are expected to be diagnosed in 2021 (Smith et al., 2019). The death rate of breast cancer is higher than any other cancer type. Early detection and treatment of breast cancer have been challenging over the last few decades. Meanwhile, deep learning algorithms using Convolutional Neural Networks to segment images have achieved considerable success in recent years. These algorithms have continued to assist in exploring the quantitative measurement of cancer cells in the tumor microenvironment. However, detecting cancerous regions in whole-slide images has been challenging as it requires substantial annotation and training efforts from clinicians and biologists. In this thesis, a notable instructing process named U-Net-based Active Learning is proposed to improve the annotation and training procedure in a feedback learning process by utilizing a Deep Convolutional Neural Networks model. The proposed approach reduces the amount of time and effort required to analyze the whole slide images. During the Active Learning process, highly uncertain samples are iteratively selected to strategically supply characteristics of the whole slide images to the training process using a low-confidence sample selection algorithm. The performance results of the proposed approach indicated that the U-Net-based Active Learning framework has promising outcomes in the feedback learning process as it reaches 88.71% AUC-ROC when only using 64 image patches, while random lymphocyte prediction reaches 84.12% AUC-ROC at maximum
One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification
The recent surge in performance for image analysis of digitised pathology
slides can largely be attributed to the advance of deep learning. Deep models
can be used to initially localise various structures in the tissue and hence
facilitate the extraction of interpretable features for biomarker discovery.
However, these models are typically trained for a single task and therefore
scale poorly as we wish to adapt the model for an increasing number of
different tasks. Also, supervised deep learning models are very data hungry and
therefore rely on large amounts of training data to perform well. In this paper
we present a multi-task learning approach for segmentation and classification
of nuclei, glands, lumen and different tissue regions that leverages data from
multiple independent data sources. While ensuring that our tasks are aligned by
the same tissue type and resolution, we enable simultaneous prediction with a
single network. As a result of feature sharing, we also show that the learned
representation can be used to improve downstream tasks, including nuclear
classification and signet ring cell detection. As part of this work, we use a
large dataset consisting of over 600K objects for segmentation and 440K patches
for classification and make the data publicly available. We use our approach to
process the colorectal subset of TCGA, consisting of 599 whole-slide images, to
localise 377 million, 900K and 2.1 million nuclei, glands and lumen
respectively. We make this resource available to remove a major barrier in the
development of explainable models for computational pathology
Scale-Equivariant UNet for Histopathology Image Segmentation
Digital histopathology slides are scanned and viewed under different
magnifications and stored as images at different resolutions. Convolutional
Neural Networks (CNNs) trained on such images at a given scale fail to
generalise to those at different scales. This inability is often addressed by
augmenting training data with re-scaled images, allowing a model with
sufficient capacity to learn the requisite patterns. Alternatively, designing
CNN filters to be scale-equivariant frees up model capacity to learn
discriminative features. In this paper, we propose the Scale-Equivariant UNet
(SEUNet) for image segmentation by building on scale-space theory. The SEUNet
contains groups of filters that are linear combinations of Gaussian basis
filters, whose scale parameters are trainable but constrained to span disjoint
scales through the layers of the network. Extensive experiments on a nuclei
segmentation dataset and a tissue type segmentation dataset demonstrate that
our method outperforms other approaches, with much fewer trainable parameters.Comment: This paper was accepted by GeoMedIA 202
FrOoDo: Framework for Out-of-Distribution Detection
FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution
detection tasks in digital pathology. It can be used with PyTorch
classification and segmentation models, and its modular design allows for easy
extension. The goal is to automate the task of OoD Evaluation such that
research can focus on the main goal of either designing new models, new methods
or evaluating a new dataset. The code can be found at
https://github.com/MECLabTUDA/FrOoDo
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