Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training Samples

• URL: http://arxiv.org/abs/2010.14782v2
• Date: Thu, 29 Oct 2020 20:35:45 GMT
• Title: Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training Samples
• Authors: Xin Ding, Qiong Zhang, William J. Welch
• Abstract summary: This work proposes a supervised learning framework to count cells from greyscale microscopic images without using annotated training images. We formulate the cell counting task as an image classification problem, where the cell counts are taken as class labels. To deal with these limitations, we propose a simple but effective data augmentation (DA) method to synthesize images for the unseen cell counts.
• Score: 20.91256120719461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern methods often formulate the counting of cells from microscopic images as a regression problem and more or less rely on expensive, manually annotated training images (e.g., dot annotations indicating the centroids of cells or segmentation masks identifying the contours of cells). This work proposes a supervised learning framework based on classification-oriented convolutional neural networks (CNNs) to count cells from greyscale microscopic images without using annotated training images. In this framework, we formulate the cell counting task as an image classification problem, where the cell counts are taken as class labels. This formulation has its limitation when some cell counts in the test stage do not appear in the training data. Moreover, the ordinal relation among cell counts is not utilized. To deal with these limitations, we propose a simple but effective data augmentation (DA) method to synthesize images for the unseen cell counts. We also introduce an ensemble method, which can not only moderate the influence of unseen cell counts but also utilize the ordinal information to improve the prediction accuracy. This framework outperforms many modern cell counting methods and won the data analysis competition (Case Study 1: Counting Cells From Microscopic Images https://ssc.ca/en/case-study/case-study-1-counting-cells-microscopic-images) of the 47th Annual Meeting of the Statistical Society of Canada (SSC). Our code is available at https://github.com/anno2020/CellCount_TinyBBBC005.

Related papers

• Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters [64.31109141089598]
  We introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data. scUNC seamlessly integrates information from different views without the need for a predefined number of clusters. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-28T08:34:58Z)
• Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images [66.79688768141814]
  We develop an automatic cell classification pipeline to label microscopy images. We then train a classification model based on the category labels. We deploy two types of segmentation models to segment cells with roundish and irregular shapes.
  arXiv  Detail & Related papers  (2023-10-22T08:11:08Z)
• Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images [57.42492501915773]
  In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Human fatigue plays a role in error making, making the use of deep learning appealing. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI) Our method successfully provides an adaptive early learning correction technique for object detection.
  arXiv  Detail & Related papers  (2022-08-05T18:52:20Z)
• Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation [16.94384366469512]
  We propose the prediction of edge-based maps for self-supervising the training of the unlabelled images. In our experiments, we show that only a small number of annotated images, e.g. 10% of the original training set, is enough for our approach to reach similar performance as with the fully annotated databases on 1- to 10-shots.
  arXiv  Detail & Related papers  (2022-08-03T14:35:00Z)
• EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring [4.555723508665994]
  We introduce a small convolutional neural network (CNN) for volumetric cell segmentation. Our model is efficient and has an asymmetric encoder-decoder structure with very few parameters in the decoder. Our method achieves top-ranking results, while our CNN model has an up to 25x lower number of parameters than other top-ranking methods.
  arXiv  Detail & Related papers  (2022-04-06T18:02:15Z)
• Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency [10.20554144865699]
  We propose a semi-supervised cell-detection method that effectively uses a time-lapse sequence with one labeled image and the other images unlabeled. First, we train a cell-detection network with a one-labeled image and estimate the unlabeled images with the trained network. We then select high-confidence positions from the estimations by tracking the detected cells from the labeled frame to those far from it.
  arXiv  Detail & Related papers  (2021-07-19T06:40:47Z)
• Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare [77.99636165307996]
  We classify biomedical images using ensembles of neural networks. We select our activations among the following ones: ReLU, leaky ReLU, Parametric ReLU, ELU, Adaptive Piecewice Linear Unit, S-Shaped ReLU, Swish, Mish, Mexican Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign.
  arXiv  Detail & Related papers  (2020-11-24T01:53:39Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation [71.50526869670716]
  We propose a two-step post-processing procedure, Split and Expand, to improve the conversion of segmentation maps to instances. In the Split step, we split clumps of cells from the segmentation map into individual cell instances with the guidance of cell-center predictions. In the Expand step, we find missing small cells using the cell-center predictions.
  arXiv  Detail & Related papers  (2020-07-21T14:05:09Z)
• Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection [6.836162272841265]
  We introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. We observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least $5.8%$ on average.
  arXiv  Detail & Related papers  (2020-05-19T11:31:53Z)
• Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy [0.20999222360659608]
  We present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. This representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types.
  arXiv  Detail & Related papers  (2020-04-03T11:55:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.