The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification

• URL: http://arxiv.org/abs/2008.12602v1
• Date: Fri, 28 Aug 2020 12:17:25 GMT
• Title: The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
• Authors: Amirreza Mahbod, Philipp Tschandl, Georg Langs, Rupert Ecker, Isabella Ellinger
• Abstract summary: We explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification. We used either manually or automatically created segmentation masks in both training and test phases in different scenarios. Our results show that using segmentation masks did not significantly improve the MM classification performance in any scenario.
• Score: 3.6516187682800547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based approaches and in particular convolutional neural networks have shown excellent classification and segmentation performances for dermatoscopic skin lesion images. These models can be trained end-to-end without requiring any hand-crafted features. However, the effect of using lesion segmentation information on classification performance has remained an open question. In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification. To do this, first, we developed a baseline classifier as the reference model without using any segmentation masks. Then, we used either manually or automatically created segmentation masks in both training and test phases in different scenarios and investigated the classification performances. Evaluated on the ISIC 2017 challenge dataset which contained two binary classification tasks (i.e. MM vs. all and seborrheic keratosis (SK) vs. all) and based on the derived area under the receiver operating characteristic curve scores, we observed four main outcomes. Our results show that 1) using segmentation masks did not significantly improve the MM classification performance in any scenario, 2) in one of the scenarios (using segmentation masks for dilated cropping), SK classification performance was significantly improved, 3) removing all background information by the segmentation masks significantly degraded the overall classification performance, and 4) in case of using the appropriate scenario (using segmentation for dilated cropping), there is no significant difference of using manually or automatically created segmentation masks.

Related papers

• 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)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments [6.677634562400846]
  Minimally invasive surgeries and related applications demand surgical tool classification and segmentation at the instance level. Our research demonstrates that while the bounding box and segmentation mask are often accurate, the classification head mis-classifies the class label of the surgical instrument. We present a new neural network framework that adds a classification module as a new stage to existing instance segmentation models.
  arXiv  Detail & Related papers  (2022-11-26T21:26:42Z)
• Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification [31.539129126161978]
  This paper introduces a novel approach called the Sub-Cluster-Aware Network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SCAN is the observation that skin disease images within a class often exhibit multiple sub-clusters. We evaluate the proposed approach on two public datasets for few-shot skin disease classification.
  arXiv  Detail & Related papers  (2022-07-03T16:06:04Z)
• Skin lesion segmentation and classification using deep learning and handcrafted features [0.0]
  We form a new type of image features, called hybrid features, which has stronger discrimination ability than single method features. Our model achieves an 92.3% balanced multiclass accuracy, which is 6.8% better than the typical single method architecture for deep learning.
  arXiv  Detail & Related papers  (2021-12-20T02:45:42Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Per-Pixel Classification is Not All You Need for Semantic Segmentation [184.2905747595058]
  Mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks. We propose MaskFormer, a simple mask classification model which predicts a set of binary masks. Our method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
  arXiv  Detail & Related papers  (2021-07-13T17:59:50Z)
• 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)
• Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs [49.32653090178743]
  convolutional neural networks (CNNs) are studied for lesion activity segmentation from two time points. CNNs are designed and evaluated that combine the information from two points in different ways. It is demonstrated that deep learning-based methods outperform classic approaches.
  arXiv  Detail & Related papers  (2020-08-05T08:49:20Z)
• Melanoma Detection using Adversarial Training and Deep Transfer Learning [6.22964000148682]
  We propose a two-stage framework for automatic classification of skin lesion images. In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis. In the second stage, we train a deep convolutional neural network for skin lesion classification.
  arXiv  Detail & Related papers  (2020-04-14T22:46:20Z)

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.