GDN: A Stacking Network Used for Skin Cancer Diagnosis [PDF]
, 2023 Skin cancer, the primary type of cancer that can be identified by visual recognition, requires an automatic identification system that can accurately classify different types of lesions. This paper presents GoogLe-Dense Network (GDN), which is an image-classification model to identify two types of skin cancer, Basal Cell Carcinoma, and Melanoma.
arxiv +1 more source
Programmed death ligand 1 expression levels, clinicopathologic features, and survival in surgically resected sarcomatoid lung carcinoma [PDF]
Asia Pac J Clin Oncol 2020 1 9, 2020 Aim: To determine the programmed death ligand-1 (PD-L1) expression rates in sarcomatoid lung carcinomas and to compare clinicopathologic features and survival rates of PD-L1-positive and negative patients. Methods: PD-L1 expression was evaluated in 65 surgically resected sarcomatoid carcinomas.
arxiv +1 more source
Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms [PDF]
Nature Scientific Reports 17912 (2017), 2017 Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment.
arxiv +1 more source
Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC) [PDF]
arXiv, 2021 Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to assist the pathologists by marking critical regions that have a high probability of exhibiting pathological features in Whole Slide Images (WSI).
arxiv
Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma [PDF]
, 2020 This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is based on convolu-tional neural networks and it is evaluated using new CNN models as well as re-trained modification of pre-existing CNN models were used. The experi-mental results showed that CNN models pre-trained on big
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Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma [PDF]
arXiv, 2020 Background: Margin assessment of basal cell carcinoma using the frozen section is a common task of pathology intraoperative consultation. Although frequently straight-forward, the determination of the presence or absence of basal cell carcinoma on the tissue sections can sometimes be challenging.
arxiv
Skin Lesion Diagnosis Using Convolutional Neural Networks [PDF]
arXiv, 2023 Cancerous skin lesions are one of the most common malignancies detected in humans, and if not detected at an early stage, they can lead to death. Therefore, it is crucial to have access to accurate results early on to optimize the chances of survival. Unfortunately, accurate results are typically obtained by highly trained dermatologists, who may not ...
arxiv
AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review [PDF]
arXiv, 2022 Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides.
arxiv
Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms [PDF]
arXiv, 2018 In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (HAM10000: 10015, PH2: 120).
arxiv
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images [PDF]
arXiv, 2019 Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving histological diagnosis by providing diagnosis suggestions, i.e. computer-assisted diagnoses is actively researched to improve safety, quality and efficiency.
arxiv