Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector

2022-03-01 · Pedro Costa, Yongpan Fu, João Nunes, Aurélio Campilho, Jaime S. Cardoso

Cancer is one of the leading causes of death in the developed world. Cancer diagnosis is performed through the microscopic analysis of a sample of suspicious tissue. This process is time consuming and error prone, but Deep Learning models could be helpful for pathologists during cancer diagnosis. We propose to change the CenterNet2 object detection model to also perform instance segmentation, which we call SegCenterNet2. We train SegCenterNet2 in the CoNIC challenge dataset and show that it performs better than Mask R-CNN in the competition metrics.

📄 PDF Abstract BibTeX arXiv:2203.01321

Code (0)

등록된 구현이 없습니다.

Tasks

Instance Segmentation object-detection Object Detection Semantic Segmentation Vocal Bursts Valence Prediction

Methods 이 논문이 사용한 방법론

RPN A Region Proposal Network, or RPN, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained…

RoIAlign Region of Interest Align, or RoIAlign, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Mask R-CNN Mask R-CNN extends Faster R-CNN to solve instance segmentation tasks. It achieves this by adding a branch for predicting an…

MGTUNet: An new UNet for colon nuclei instance segmentation and quantification

2022-10-20 · Liangrui Pan, Lian Wang, Zhichao Feng, Zhujun Xu 외

Colorectal cancer (CRC) is among the top three malignant tumor types in terms of morbidity and mortality. Histopathological images are the gold standard for diagnosing colon cancer. Cellular nuclei instance segmentation …

Instance Segmentation regression Segmentation Semantic Segmentation

Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting

2022-03-01 · Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu 외

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…

Classification Explainable Models Nuclear Segmentation Segmentation

Nuclei instance segmentation and classification in histopathology images with StarDist

2022-03-03 · Martin Weigert, Uwe Schmidt

Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally developed for fluorescence microscopy, can …

Classification Instance Segmentation Segmentation Semantic Segmentation

Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting

2022-03-01 · Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb

We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational patholog…

Instance Segmentation Nuclear Segmentation Segmentation Semantic Segmentation

Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification

2021-08-25 · Simon Graham, Mostafa Jahanifar, Ayesha Azam, Mohammed Nimir 외

The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers. Yet, there is a major bottleneck in the success of such approac…

Instance Segmentation Semantic Segmentation