Patch-Based Cervical Cancer Segmentation using Distance from Boundary of
Tissue
- URL: http://arxiv.org/abs/2108.08508v1
- Date: Thu, 19 Aug 2021 05:41:18 GMT
- Title: Patch-Based Cervical Cancer Segmentation using Distance from Boundary of
Tissue
- Authors: Kengo Araki, Mariyo Rokutan-Kurata, Kazuhiro Terada, Akihiko Yoshizawa
and Ryoma Bise
- Abstract summary: The distance from the Boundary of tissue (DfB) is global information that can be extracted from the original image.
We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.
- Score: 8.137198664755598
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pathological diagnosis is used for examining cancer in detail, and its
automation is in demand. To automatically segment each cancer area, a
patch-based approach is usually used since a Whole Slide Image (WSI) is huge.
However, this approach loses the global information needed to distinguish
between classes. In this paper, we utilized the Distance from the Boundary of
tissue (DfB), which is global information that can be extracted from the
original image. We experimentally applied our method to the three-class
classification of cervical cancer, and found that it improved the total
performance compared with the conventional method.
Related papers
- Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification [50.899861205016265]
We propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification.
Our framework introduces two key components into the common MIL model architecture.
We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets.
arXiv Detail & Related papers (2025-03-08T04:51:58Z) - Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections [21.75452517154339]
We reprocessed 1,399 whole slide images (WSIs) and labels from the Camelyon-16 and Camelyon-17 datasets.
Based on the sizes of re-annotated tumor regions, we upgraded the binary cancer screening task to a four-class task.
arXiv Detail & Related papers (2024-11-16T09:19:24Z) - Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology [49.518701946822446]
We propose a simple approach to improve OOD generalisation for cancer detection by focusing on nuclear morphology and organisation.
Our approach integrates original images with nuclear segmentation masks during training, encouraging the model to prioritise nuclei.
We show, using multiple datasets, that our method improves OOD generalisation and also leads to increased robustness to image corruptions and adversarial attacks.
arXiv Detail & Related papers (2024-11-14T11:27:15Z) - Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development [59.74920439478643]
In this paper, we collect and annotated the first benchmark dataset that covers diverse ERUS scenarios.
Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames.
We introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR)
arXiv Detail & Related papers (2024-08-19T15:04:42Z) - CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark
Model for Rectal Cancer Segmentation [8.728236864462302]
Rectal cancer segmentation of CT image plays a crucial role in timely clinical diagnosis, radiotherapy treatment, and follow-up.
These obstacles arise from the intricate anatomical structures of the rectum and the difficulties in performing differential diagnosis of rectal cancer.
To address these issues, this work introduces a novel large scale rectal cancer CT image dataset CARE with pixel-level annotations for both normal and cancerous rectum.
We also propose a novel medical cancer lesion segmentation benchmark model named U-SAM.
The model is specifically designed to tackle the challenges posed by the intricate anatomical structures of abdominal organs by incorporating prompt information.
arXiv Detail & Related papers (2023-08-16T10:51:27Z) - Robust Tumor Detection from Coarse Annotations via Multi-Magnification
Ensembles [11.070094685209598]
We present a novel ensemble method that significantly improves the detection accuracy of metastasis on the open CAMELYON16 data set of sentinel lymph nodes of breast cancer patients.
Our experiments show that better results can be achieved with our technique making it clinically feasible to use for cancer diagnosis.
arXiv Detail & Related papers (2023-03-29T08:41:22Z) - Stain-invariant self supervised learning for histopathology image
analysis [74.98663573628743]
We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer.
Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
arXiv Detail & Related papers (2022-11-14T18:16:36Z) - A Pathologist-Informed Workflow for Classification of Prostate Glands in
Histopathology [62.997667081978825]
Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides.
Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands.
This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
arXiv Detail & Related papers (2022-09-27T14:08:19Z) - Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for
Cervical Cytology Image Classification [1.6986898305640261]
We propose a fully automated computer-aided diagnosis tool for classifying single-cell and slide images of cervical cancer.
We use the Sugeno Fuzzy Integral to ensemble the decision scores from three popular deep learning models, namely, Inception v3, DenseNet-161 and ResNet-34.
arXiv Detail & Related papers (2021-08-21T08:41:41Z) - Weakly supervised multiple instance learning histopathological tumor
segmentation [51.085268272912415]
We propose a weakly supervised framework for whole slide imaging segmentation.
We exploit a multiple instance learning scheme for training models.
The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
arXiv Detail & Related papers (2020-04-10T13:12:47Z) - Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size.
The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
arXiv Detail & Related papers (2020-02-03T22:25:01Z) - Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype
Classification with Unannotated Histopathological Images [16.02231907106384]
We develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks.
The classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists.
arXiv Detail & Related papers (2020-01-06T14:09:51Z)
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.