mlsanityData to 14 Oct 2026

Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images -- Nevus & Melanoma

arXiv 2405.09851 ↗First posted 16 May 2024eess.IV cs.CV q-bio.QMcited by 0

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Abstract (arXiv)

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort that contains 160 hematoxylin and eosin whole-slide images of primary melanomas (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep-learning method to allow for classification, at the slide level, of nevi and melanomas. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 16 May 2024.

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References · 14

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  12. 2019Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networkscited by 43
2 more
  1. 20173D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumescited by 38
  2. 2020Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation studycited by 3

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