Deep learning-based accurate diagnosis of eyelid malignant melanoma from gigapixel pathologic slides

Ding, Longqian; Wang, Linyan; Huang, Xiwei; Wang, Yaqi; Ye, Juan; Sun, Lingling

doi:10.1117/12.2524179

Cited by 2 publications

(1 citation statement)

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“…Although computer vision has made some progress in the field of tumor segmentation, automated analysis studies based on eyelid tumor pathology are very rare due to the lack of dataset. In 2018, Ding et al designed a study using CNN for the binary classification of malignant melanoma (MM) and the whole slide image-level classification was realized using a random forest classifier to assist pathologists in diagnosis [ 23 ]. In 2020, Wang et al trained CNN on patch-level classification and used malignant probability to embed patches into each WSI to generate visualized heatmaps and also established a random forest model to establish WSI-level diagnosis [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

MAC-ResNet: Knowledge Distillation Based Lightweight Multiscale-Attention-Crop-ResNet for Eyelid Tumors Detection and Classification

Huang

Yao²,

Xing³

et al. 2022

JPM

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Eyelid tumors are tumors that occur in the eye and its appendages, affecting vision and appearance, causing blindness and disability, and some having a high lethality rate. Pathological images of eyelid tumors are characterized by large pixels, multiple scales, and similar features. Solving the problem of difficult and time-consuming fine-grained classification of pathological images is important to improve the efficiency and quality of pathological diagnosis. The morphology of Basal Cell Carcinoma (BCC), Meibomian Gland Carcinoma (MGC), and Cutaneous Melanoma (CM) in eyelid tumors are very similar, and it is easy to be misdiagnosed among each category. In addition, the diseased area, which is decisive for the diagnosis of the disease, usually occupies only a relatively minor portion of the entire pathology section, and screening the area of interest is a tedious and time-consuming task. In this paper, deep learning techniques to investigate the pathological images of eyelid tumors. Inspired by the knowledge distillation process, we propose the Multiscale-Attention-Crop-ResNet (MAC-ResNet) network model to achieve the automatic classification of three malignant tumors and the automatic localization of whole slide imaging (WSI) lesion regions using U-Net. The final accuracy rates of the three classification problems of eyelid tumors on MAC-ResNet were 96.8%, 94.6%, and 90.8%, respectively.

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Section: Related Workmentioning

confidence: 99%