Accurate detection of oral cancer is crucial for improving patient outcomes. However, the field faces two key challenges: the scarcity of deep learning-based image segmentation research specifically targeting oral cancer and the lack of annotated data. Our study proposes OCU-Net, a pioneering U-Net image segmentation architecture exclusively designed to detect oral cancer in hematoxylin and eosin (H&E) stained image datasets. OCU-Net incorporates advanced deep learning modules, such as the Channel and Spatial Attention Fusion (CSAF) module, a novel and innovative feature that emphasizes important channel and spatial areas in H&E images while exploring contextual information. In addition, OCU-Net integrates other innovative components such as Squeeze-and-Excite (SE) attention module, Atrous Spatial Pyramid Pooling (ASPP) module, residual blocks, and multi-scale fusion. The incorporation of these modules showed superior performance for oral cancer segmentation for two datasets used in this research. Furthermore, we effectively utilized the efficient ImageNet pre-trained MobileNet-V2 model as a backbone of our OCU-Net to create OCU-Netm, an enhanced version achieving state-of-the-art results. Comprehensive evaluation demonstrates that OCU-Net and OCU-Netm outperformed existing segmentation methods, highlighting their precision in identifying cancer cells in H&E images from OCDC and ORCA datasets.
In this work we show preliminary results of deep multi-task learning in the area of computational pathology. We combine 11 tasks ranging from patch-wise oral cancer classification, one of the most prevalent cancers in the developing world, to multi-tissue nuclei instance segmentation and classification.
Computer-aided diagnosis (CAD) can be used as an important tool to aid and enhance pathologists' diagnostic decision-making. Deep learning techniques, such as convolutional neural networks (CNN) and fully convolutional networks (FCN), have been successfully applied in medical and biological research. Unfortunately, histological image segmentation is often constrained by the availability of labeled training data once labeling histological images for segmentation purposes is a highly-skilled, complex, and time-consuming task. This paper presents the hematoxylin and eosin (H&E) stained oral cavit…
School of Bioengineering Sciences and Research, MIT ADT University（MIT ADT大学生物工程科学与研究学院）；Department of Oral and Maxillofacial Pathology, Sinhgad Dental College and Hospital（辛加德牙科学院与医院口腔颌面病理学系）；Department of Computer Science, Stony Brook University（石溪大学计算机科学系）
Oral cancer has more than 83% survival rate if detected in its early stages, however, only 29% of cases are currently detected early. Deep learning techniques can detect patterns of oral cancer cells and can aid in its early detection. In this work, we present the first results of neural networks for oral cancer detection using microscopic images. We compare numerous state-of-the-art models via transfer learning approach and collect and release an augmented dataset of high-quality microscopic images of oral cancer. We present a comprehensive study of different models and report their performan…
King Khalid University（哈立德国王大学）；Taif University（塔伊夫大学）
Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of specialists in such areas. Automating the initial screening process using artificial intelligence (AI) to detect pre-cancerous lesions can prove to be an effective and inexpensive technique that would allow patients to be triaged accordingly to receive appropriate clinical management.…
Uppsala University（乌普萨拉大学）；Karolinska Universitetsjukhuset（卡罗林斯卡大学医院）
Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification. Our novel focus selection step provides fast per-cell focus decisions at human-level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cy…
Uppsala University（乌普萨拉大学）；Pathology and Cytology Dalarna, County Hospital Falun（法伦县医院达拉纳病理与细胞学）；Clinical Research Center Dalarna, Uppsala University（乌普萨拉大学达拉纳临床研究中心）
The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample from the oral cavity. This process is time consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Skilled cytotechnologists are able to detect changes due to malignancy, however, to introduce this approach into clinical routine is associated with challenges such as a lack of experts and labour-intensive work. To design a trustworthy OC detection system that would assist cytotechnologists, we are interested in AI-ba…
Charles Sturt University（查尔斯特大学）；Al Iraqia University（伊拉克大学）；Western Sydney University（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；Asia Pacific International College（亚太国际学院）；Worcester State University（伍斯特州立大学）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；Greater Western Sydney Area Health Services（大西悉尼地区卫生服务局）
Background and Aim: Over-fitting issue has been the reason behind deep learning technology not being successfully implemented in oral cancer images classification. The aims of this research were reducing overfitting for accurately producing the required dimension reduction feature map through Deep Learning algorithm using Convolutional Neural Network. Methodology: The proposed system consists of Enhanced Convolutional Neural Network that uses an autoencoder technique to increase the efficiency of the feature extraction process and compresses information. In this technique, unpooling and deconv…
Explainable Multimodal Deep Learning Integrating Imaging and Clinical Data for Oral Potentially Malignant Disorder Detection
Wyant College of Optical Sciences, University of Arizona（亚利桑那大学怀恩特光学科学学院）；Beckman Laser Institute & Medical Clinic, University of California Irvine（加州大学尔湾分校贝克曼激光研究所与医学诊所）
Fused electrical impedance tomography using an electromagnetically tracked handheld probe for oral cancer margin assessment: reconstruction methods and phantom validation
Thayer School of Engineering, Dartmouth College（达特茅斯学院泰勒工程学院）；Geisel School of Medicine, Dartmouth College（达特茅斯学院盖泽尔医学院）
A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma
Indian Council of Medical Research-National Institute for Research in Digital Health & Data Science（印度医学研究理事会-数字健康与数据科学国家研究所）；Koita Centre of Digital Health（数字健康中心）；IIT Bombay（印度理工学院-博伊斯）；Department of Electrical Engineering（电气工程系）；Division of Non Communicable Diseases（非传染性疾病部门）；Indian Council of Medical Research（印度医学研究理事会）；Regional Institute of Medical Sciences（地区医学科学研究所）；Department of Pathology（病理学系）；Pt BD Sharma PGIMS（Pt BD Sharma PGIMS医院）；Institute of Pathology（病理研究所）；Madras Medical College（马德拉斯医学院）；SMS Medical College Jaipur（杰伊普尔SMS医学院）；Department of Otorhinolaryngology（耳鼻喉科系）
A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis
School of Software Engineering, South China University of Technology（华南理工大学软件工程学院）；Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学口腔颌面外科医院）；Mohamed Bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；School of Computer Science, University of Adelaide（阿德莱德大学计算机科学学院）
Purpose: Body composition is known to be associated with many diseases including diabetes, cancers and cardiovascular diseases. In this paper, we developed a fully automatic body tissue decomposition procedure to segment three major compartments that are related to body composition analysis - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and muscle. Three additional compartments - the ventral cavity, lung and bones were also segmented during the segmentation process to assist segmentation of the major compartments. Methods: A convolutional neural network (CNN) model with den…
