Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

• URL: http://arxiv.org/abs/2009.05403v1
• Date: Thu, 10 Sep 2020 13:49:38 GMT
• Title: Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
• Authors: J\'er\'emy Scheurer, Claudio Ferrari, Luis Berenguer Todo Bom, Michaela Beer, Werner Kempf, Luis Haug
• Abstract summary: Mycosis fungoides (MF) is a rare, potentially life threatening skin disease. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists.
• Score: 4.4154284772781525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.

Related papers

• Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction [6.993091116816899]
  Myopic macular degeneration is the primary cause of vision loss in individuals with pathological myopia. Early detection and prompt treatment are crucial in preventing vision impairment due to myopic maculopathy. This was the focus of the Myopic Maculopathy Analysis Challenge (MMAC)
  arXiv  Detail & Related papers  (2024-01-08T00:26:21Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion [4.819821513256158]
  LCAUnet is proposed to improve the ability of complementary representation with fusion of edge and body features. Experiments on public available dataset ISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms most state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-01T14:05:53Z)
• Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography [53.38589633687604]
  We propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it.
  arXiv  Detail & Related papers  (2023-04-26T14:11:04Z)
• Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images [8.404881822414898]
  Digital whole slide images (WSIs) are generally captured at microscopic resolution and encompass extensive spatial data. We propose a novel dual attention approach, consisting of two main components, both inspired by the visual examination process of a pathologist. We show that the proposed model achieves performance better than or comparable to the state-of-the-art methods while processing less than 10% of the WSI at the highest magnification.
  arXiv  Detail & Related papers  (2023-02-19T22:26:25Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC) [2.031570465477242]
  We focus on detecting Basal Cell Carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. We analyze two different encoders for the first part of the UNet network and two additional training strategies. The best model achieves over 96%, accuracy, sensitivity, and specificity on the test set.
  arXiv  Detail & Related papers  (2021-03-05T15:39:55Z)
• Lesion Net -- Skin Lesion Segmentation Using Coordinate Convolution and Deep Residual Units [18.908448254745473]
  The accuracy of segmenting melanomas skin lesions is quite a challenging task due to less data for training, irregular shapes, unclear boundaries, and different skin colors. Our proposed approach helps in improving the accuracy of skin lesion segmentation. The results show that the proposed model either outperform or at par with the existing skin lesion segmentation methods.
  arXiv  Detail & Related papers  (2020-12-28T14:43:04Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)

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.