Bladder segmentation based on deep learning approaches: current limitations and lessons

• URL: http://arxiv.org/abs/2101.06498v1
• Date: Sat, 16 Jan 2021 18:20:23 GMT
• Title: Bladder segmentation based on deep learning approaches: current limitations and lessons
• Authors: Mark G. Bandyk, Dheeraj R Gopireddy, Chandana Lall, K.C. Balaji, Jose Dolz
• Abstract summary: We provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.
• Score: 4.51983747559299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumour. Hence, multi region segmentation on patients presenting with symptoms of bladder tumours using deep learning heralds a new level of staging accuracy and prediction of the biologic behaviour of the tumour. Nevertheless, despite the success of these models in other medical problems, progress in multi region bladder segmentation is still at a nascent stage, with just a handful of works tackling a multi region scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.

Related papers

• AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review [17.187976904150545]
  Early detection is vital in reducing the mortality rate among prostate cancer patients. Prostate segmentation is challenging due to imperfections in the images and the prostate's complex tissue structure. Recent machine learning and data mining tools have been integrated into various medical areas, including image segmentation.
  arXiv  Detail & Related papers  (2024-07-09T07:36:18Z)
• NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning [1.3911081328487294]
  We introduce a pipeline designed for bladder cancer grading using histological slides. It extracts urothelium tissue tiles at different magnification levels, employing a convolutional neural network for processing for feature extraction. It employs a nested multiple instance learning approach with attention to predict the grade.
  arXiv  Detail & Related papers  (2024-05-24T07:02:39Z)
• Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations [49.33388736227072]
  We propose a semi- and weakly-supervised learning framework for mass segmentation. We use limited strongly-labeled samples and sufficient weakly-labeled samples to achieve satisfactory performance. Experiments on CBIS-DDSM and INbreast datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-03-14T12:05:25Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• 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)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• 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)
• End-to-end Learning for Image-based Detection of Molecular Alterations in Digital Pathology [1.916179040410189]
  Current approaches for classification of whole slide images (WSI) in digital pathology predominantly utilize a two-stage learning pipeline. A major drawback of such approaches is the requirement for task-specific auxiliary labels which are not acquired in clinical routine. We propose a novel learning pipeline for WSI classification that is trainable end-to-end and does not require any auxiliary annotations.
  arXiv  Detail & Related papers  (2022-06-30T20:30:33Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images [1.244681179922733]
  We present a self-learning framework to grade bladder cancer from histological images stained viachemical techniques. We propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows classifying histological patches into different levels of the disease.
  arXiv  Detail & Related papers  (2021-06-25T11:04:04Z)
• 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)

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.