Classification of Melanoma Using Different Segmentation Techniques

Gulati, Savy; Bhogal, Rosepreet Kaur

doi:10.1007/978-3-030-16681-6_45

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research results presented indicate a positive correlation between enhanced segmentation quality and improved classification accuracy [ 6 , 7 , 8 ]. Our other work has demonstrated that using a segmentation mask for skin lesion classification enhances classification accuracy, and the quality of the used segmentation mask directly influences classification results [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Skin Lesion Segmentation with Self-Training

Dzieniszewska,

Garbat,

Piramidowicz

2024

Cancers

View full text Add to dashboard Cite

Skin lesion segmentation plays a key role in the diagnosis of skin cancer; it can be a component in both traditional algorithms and end-to-end approaches. The quality of segmentation directly impacts the accuracy of classification; however, attaining optimal segmentation necessitates a substantial amount of labeled data. Semi-supervised learning allows for employing unlabeled data to enhance the results of the machine learning model. In the case of medical image segmentation, acquiring detailed annotation is time-consuming and costly and requires skilled individuals so the utilization of unlabeled data allows for a significant mitigation of manual segmentation efforts. This study proposes a novel approach to semi-supervised skin lesion segmentation using self-training with a Noisy Student. This approach allows for utilizing large amounts of available unlabeled images. It consists of four steps—first, training the teacher model on labeled data only, then generating pseudo-labels with the teacher model, training the student model on both labeled and pseudo-labeled data, and lastly, training the student* model on pseudo-labels generated with the student model. In this work, we implemented DeepLabV3 architecture as both teacher and student models. As a final result, we achieved a mIoU of 88.0% on the ISIC 2018 dataset and a mIoU of 87.54% on the PH2 dataset. The evaluation of the proposed approach shows that Noisy Student training improves the segmentation performance of neural networks in a skin lesion segmentation task while using only small amounts of labeled data.

show abstract