[SSCI 2025] Oral cancer recognition on photographic images via deep learning semantic segmentation
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Updated
Jun 19, 2025 - Python
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[SSCI 2025] Oral cancer recognition on photographic images via deep learning semantic segmentation
Code for "Image-based screening of oral cancer via deep ensemble architecture" (SSCI 2023)
[Nature Portfolio] The official code for "A high-order focus interaction model and oral ulcer dataset for oral ulcer segmentation".
[SSCI 2025] Improving oral cancer classification via segment-driven photographic deep learning imaging
Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning
This repository accompanies the article entitled "Automated Classification of Oral Cancer Lesions: Vision Transformer vs Radiomics."
Model for early detection of Oral cancer via Histopathological Image datasets
Deep learning workflow, that exploiting different models is able to classify and explain the classification
A deep learning project for classifying oral cancer images. The name "UMLomo" is derived from the term for "oral" in some languages, reflecting the project's focus. This repository contains a deep learning model to aid in the early detection and diagnosis of oral cancer from images.
Oral Cancer diagnosis
Code for classification of cancerous vs. benign oral lesions using machine learning
Deep transfer learning models to identify oral tongue cancer from limited public datasets, utilizing Grad-CAM for model explainability
This repository can be used to reproduce and/or update the tables and figures of our publication titled "Patterns of Lymph Node Involvement for Oral Cavity Squamous Cell Carcinoma".
Oral Cancer Detection using U-Net and YOLOv8 A deep learning pipeline combining U-Net for lesion segmentation and YOLOv8 for object detection on oral cavity images. Designed to assist early diagnosis of oral cancer from clinical photographs.
RSANet: An advance in oral cancer prediction using residual network with soft-attention mechanism
ORAL Cancer Detetecting AI
Oral Cancer Classifier is an advanced image classification project focused on detecting oral cancer from histopathologic images. Leveraging the power of Convolutional Neural Networks (CNNs), this project transforms pixel-level data from oral tissue samples into actionable insights for early detection of cancer.
Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection —— Computers in Biology and Medicine
Repository for oral cancer recognition experiment
Tool for Evaluating Deep Learning Models in Histopathological Image Analysis - A MATLAB-based User Interface [Tentative Version]
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