You should use regression to detect cells
Automated cell detection in histopathology images is a hard problem due to the large
variance of cell shape and appearance. We show that cells can be detected reliably in …
variance of cell shape and appearance. We show that cells can be detected reliably in …
Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: A case study on HeLa line
A Ghaznavi, R Rychtáriková, M Saberioon… - Computers in Biology and …, 2022 - Elsevier
Living cell segmentation from bright-field light microscopy images is challenging due to the
image complexity and temporal changes in the living cells. Recently developed deep …
image complexity and temporal changes in the living cells. Recently developed deep …
Gaussian process density counting from weak supervision
M von Borstel, M Kandemir, P Schmidt, MK Rao… - Computer Vision–ECCV …, 2016 - Springer
As a novel learning setup, we introduce learning to count objects within an image from only
region-level count information. This level of supervision is weaker than earlier approaches …
region-level count information. This level of supervision is weaker than earlier approaches …
Microscopy
Medical Engineering I Page 1 Chapter 5 Microscopy Authors: Firas Mualla, Marc Aubreville,
and Andreas Maier 5.1 Image Formation in a Thin Lens . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.2 …
and Andreas Maier 5.1 Image Formation in a Thin Lens . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.2 …
Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images
A Ghaznavi, R Rychtáriková, P Císař, MM Ziaei, D Štys - Symmetry, 2024 - mdpi.com
Multi-class segmentation of unlabelled living cells in time-lapse light microscopy images is
challenging due to the temporal behaviour and changes in cell life cycles and the complexity …
challenging due to the temporal behaviour and changes in cell life cycles and the complexity …
Unbiased evaluation of keypoint detectors with respect to rotation invariance
K Matusiak, P Skulimowski, P Strumillo - IET Computer Vision, 2017 - Wiley Online Library
The authors present the results of a comparative performance study of algorithms for
detecting keypoints in digital images. The Harris, good features to track (GFTT), SIFT, SURF …
detecting keypoints in digital images. The Harris, good features to track (GFTT), SIFT, SURF …
Phase contrast cell detection using multilevel classification
In this paper, we propose a fully automated learning‐based approach for detecting cells in
time‐lapse phase contrast images. The proposed system combines 2 machine learning …
time‐lapse phase contrast images. The proposed system combines 2 machine learning …
Graph-embedded online learning for cell detection and tumour proportion score estimation
J Chen, Y Zhu, Z Chen - Electronics, 2022 - mdpi.com
Cell detection in microscopy images can provide useful clinical information. Most methods
based on deep learning for cell detection are fully supervised. Without enough labelled …
based on deep learning for cell detection are fully supervised. Without enough labelled …
Cell detection with deep learning accelerated by sparse kernel
As lung cancer is one of the most frequent and serious disease causing death for both men
and women, early diagnosis and differentiation of lung cancers is clinically important …
and women, early diagnosis and differentiation of lung cancers is clinically important …
A multi-stage random forest classifier for phase contrast cell segmentation
We present a machine learning based approach to automatically detect and segment cells
in phase contrast images. The proposed method consists of a multi-stage classification …
in phase contrast images. The proposed method consists of a multi-stage classification …