2,657 research outputs found
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.
In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Counting of mitotic figures is a fundamental step in grading and
prognostication of several cancers. However, manual mitosis counting is tedious
and time-consuming. In addition, variation in the appearance of mitotic figures
causes a high degree of discordance among pathologists. With advances in deep
learning models, several automatic mitosis detection algorithms have been
proposed but they are sensitive to {\em domain shift} often seen in histology
images. We propose a robust and efficient two-stage mitosis detection
framework, which comprises mitosis candidate segmentation ({\em Detecting
Fast}) and candidate refinement ({\em Detecting Slow}) stages. The proposed
candidate segmentation model, termed \textit{EUNet}, is fast and accurate due
to its architectural design. EUNet can precisely segment candidates at a lower
resolution to considerably speed up candidate detection. Candidates are then
refined using a deeper classifier network, EfficientNet-B7, in the second
stage. We make sure both stages are robust against domain shift by
incorporating domain generalization methods. We demonstrate state-of-the-art
performance and generalizability of the proposed model on the three largest
publicly available mitosis datasets, winning the two mitosis domain
generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase
the utility of the proposed algorithm by processing the TCGA breast cancer
cohort (1,125 whole-slide images) to generate and release a repository of more
than 620K mitotic figures.Comment: Extended version of the work done for MIDOG challenge submissio
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
This paper explores the problem of breast tissue classification of microscopy
images. Based on the predominant cancer type the goal is to classify images
into four categories of normal, benign, in situ carcinoma, and invasive
carcinoma. Given a suitable training dataset, we utilize deep learning
techniques to address the classification problem. Due to the large size of each
image in the training dataset, we propose a patch-based technique which
consists of two consecutive convolutional neural networks. The first
"patch-wise" network acts as an auto-encoder that extracts the most salient
features of image patches while the second "image-wise" network performs
classification of the whole image. The first network is pre-trained and aimed
at extracting local information while the second network obtains global
information of an input image. We trained the networks using the ICIAR 2018
grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method
yields 95 % accuracy on the validation set compared to previously reported 77 %
accuracy rates in the literature. Our code is publicly available at
https://github.com/ImagingLab/ICIAR2018Comment: 10 pages, 5 figures, ICIAR 2018 conferenc
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
- …