Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation

• URL: http://arxiv.org/abs/2010.13454v2
• Date: Sun, 21 Feb 2021 01:06:00 GMT
• Title: Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation
• Authors: Kumar Abhishek, Ghassan Hamarneh
• Abstract summary: Deep learning-based models are susceptible to class imbalance in the data. We propose a novel metric-based loss function using the Matthews correlation coefficient, a metric that has been shown to be efficient in scenarios with skewed class distributions. We show that the proposed loss function outperform those trained using Dice loss by 11.25%, 4.87%, and 0.76% respectively in the mean Jaccard index.
• Score: 19.673662082910766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The segmentation of skin lesions is a crucial task in clinical decision support systems for the computer aided diagnosis of skin lesions. Although deep learning-based approaches have improved segmentation performance, these models are often susceptible to class imbalance in the data, particularly, the fraction of the image occupied by the background healthy skin. Despite variations of the popular Dice loss function being proposed to tackle the class imbalance problem, the Dice loss formulation does not penalize misclassifications of the background pixels. We propose a novel metric-based loss function using the Matthews correlation coefficient, a metric that has been shown to be efficient in scenarios with skewed class distributions, and use it to optimize deep segmentation models. Evaluations on three skin lesion image datasets: the ISBI ISIC 2017 Skin Lesion Segmentation Challenge dataset, the DermoFit Image Library, and the PH2 dataset, show that models trained using the proposed loss function outperform those trained using Dice loss by 11.25%, 4.87%, and 0.76% respectively in the mean Jaccard index. The code is available at https://github.com/kakumarabhishek/MCC-Loss.

Related papers

• Robust T-Loss for Medical Image Segmentation [56.524774292536264]
  This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data. Our experiments show that the T-Loss outperforms traditional loss functions in terms of dice scores on two public medical datasets.
  arXiv  Detail & Related papers  (2023-06-01T14:49:40Z)
• Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation [1.1470070927586016]
  We compare Binary Cross Entropy, Dice, Tversky, and Combo loss using the deep learning architectures (i.e. U-Net, Attention U-Net, and Nested UNet) with the DRIVE dataset. The results showed that there is a significant difference in the selection of loss function.
  arXiv  Detail & Related papers  (2022-06-01T14:47:18Z)
• Meta Balanced Network for Fair Face Recognition [51.813457201437195]
  We systematically and scientifically study bias from both data and algorithm aspects. We propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss. Extensive experiments show that MBN successfully mitigates bias and learns more balanced performance for people with different skin tones in face recognition.
  arXiv  Detail & Related papers  (2022-05-13T10:25:44Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• 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)
• Offset Curves Loss for Imbalanced Problem in Medical Segmentation [15.663236378920637]
  We develop a new deep learning-based model which takes into account both higher feature level i.e. region inside contour and lower feature level i.e. contour. Our proposed Offset Curves (OsC) loss consists of three main fitting terms. We evaluate our proposed OsC loss on both 2D network and 3D network.
  arXiv  Detail & Related papers  (2020-12-04T08:35:21Z)
• 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)
• Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation [50.63623720394348]
  We propose a loss reweighting approach to increase the ability of the network to detect small lesions. We report the benefit from our method for well-known loss functions, including Dice Loss, Focal Loss, and Asymmetric Similarity Loss. Our experiments show that inverse weighting considerably increases the detection quality, while preserves the delineation quality on a state-of-the-art level.
  arXiv  Detail & Related papers  (2020-07-20T12:08:22Z)
• Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction [65.44752447868626]
  We propose collaborative boundaryaware context encoding networks called AEP-Net for error prediction task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. The AEP-Net achieves an average DSC of 0.8358, 0.8164 for error prediction task, and shows a high Pearson correlation coefficient of 0.9873.
  arXiv  Detail & Related papers  (2020-06-25T12:42:01Z)

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.