Oral cancer ranks among the most prevalent cancers globally, with a particularly high mortality rate in regions lacking adequate healthcare access. Early diagnosis is crucial for reducing mortality; however, challenges persist due to limited oral health programs, inadequate infrastructure, and a shortage of healthcare practitioners. Conventional deep learning models, while promising, often rely on point estimates, leading to overconfidence and reduced reliability. Critically, these models require large datasets to mitigate overfitting and ensure generalizability, an unrealistic demand in settings with limited training data. To address these issues, we propose a hybrid model that combines a convolutional neural network (CNN) with Bayesian deep learning for oral cancer classification using small training sets. This approach employs variational inference to enhance reliability through uncertainty quantification. The model was trained on photographic color images captured by smartphones and evaluated on three distinct test datasets. The proposed method achieved 94% accuracy on a test dataset with a distribution similar to that of the training data, comparable to traditional CNN performance. Notably, for real-world photographic image data, despite limitations and variations differing from the training dataset, the proposed model demonstrated superior generalizability, achieving 88% accuracy on diverse datasets compared to 72.94% for traditional CNNs, even with a smaller dataset. Confidence analysis revealed that the model exhibits low uncertainty (high confidence) for correctly classified samples and high uncertainty (low confidence) for misclassified samples. These results underscore the effectiveness of Bayesian inference in data-scarce environments in enhancing early oral cancer diagnosis by improving model reliability and generalizability.
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…
University of Amsterdam（阿姆斯特丹大学）；Delft University of Technology（代尔夫特理工大学）；Netherlands Cancer Institute（荷兰癌症研究所）
Oral cancer speech is a disease which impacts more than half a million people worldwide every year. Analysis of oral cancer speech has so far focused on read speech. In this paper, we 1) present and 2) analyse a three-hour long spontaneous oral cancer speech dataset collected from YouTube. 3) We set baselines for an oral cancer speech detection task on this dataset. The analysis of these explainable machine learning baselines shows that sibilants and stop consonants are the most important indicators for spontaneous oral cancer speech detection.
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…
Uncertainty-Aware Multimodal Fusion for Oral Lesion Classification
1 School of Medical Science \& Technology, IIT Kharagpur, India 2 Department of Electrical Engineering, IIT Kharagpur, India 3 Dr. B.C. Roy Multispeciality Medical Research Centre, IIT Kharagpur, India
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…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University Medical Center Erlangen（埃尔朗根大学医学中心）；Friedrich-Schiller-Universität Jena（耶拿大学）；University Medical Center Mainz（美因茨大学医学中心）；Johannes Gutenberg-Universität Mainz（美因茨约翰内斯·古腾堡大学）
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and an reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-tim…
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…
United International University（联合国际大学）；The University of Western Australia（西澳大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；Ahsanullah University of Science and Technology（阿萨努拉科技大学）；Charles Darwin University（查尔斯达尔文大学）
Skin cancer is a major concern to public health, accounting for one-third of the reported cancers. If not detected early, the cancer has the potential for severe consequences. Recognizing the critical need for effective skin cancer classification, we address the limitations of existing models, which are often too large to deploy in areas with limited computational resources. In response, we present a knowledge distillation based approach for creating a lightweight yet high-performing classifier. The proposed solution involves fusing three models, namely ResNet152V2, ConvNeXtBase, and ViT Base,…
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.
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）
EPFL（洛桑联邦理工学院）；Bern（伯尔尼大学）；Lausanne University Hospital（洛桑大学医院）；Center of Biomedical Imaging（生物医学成像中心）
With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a represen…
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…
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）
Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques
Capgemini Engineering（凯捷工程）；Université Rouen Normandie（诺曼底鲁昂大学）；Hôpitaux Universitaires de Strasbourg（斯特拉斯堡大学医院）；IT University of Copenhagen（哥本哈根信息技术大学）；Université de Strasbourg（斯特拉斯堡大学）；Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC)（遗传学与分子细胞生物学研究所）
Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. However, the classification accuracy of these models still tends to be severely limited by the scarcity of representative images from malignant tumours. We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with aux…
Hamburg University of Technology（汉堡工业大学）；University of Bonn（波恩大学）；Klinikum Braunschweig（布伦瑞克医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
Early detection of head and neck tumors is crucial for patient survival. Often, diagnoses are made based on endoscopic examination of the larynx followed by biopsy and histological analysis, leading to a high inter-observer variability due to subjective assessment. In this regard, early non-invasive diagnostics independent of the clinician would be a valuable tool. A recent study has shown that hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors, as precancerous or cancerous lesions show specific spectral signatures that distinguish them from healthy tiss…
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）
Seoul National University（首尔国立大学）；Bioinformatics Institute, Seoul National University（首尔国立大学生物信息学研究所）
Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Rece…
Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture analysis, especially focusing on tumour-stroma ratio. In this work, we automate the task of tissue classification within colorectal cancer histology samples using deep transfer learning. We use discriminative fine-tuning with one-cycle-policy and apply structure-preserving colour normalization to boost our results. We also provide visual explanations of the deep neural network's …
Resource-Limited Automated Ki67 Index Estimation in Breast Cancer
Università degli Studi di Milano（米兰大学）；European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）；Humanitas University（人道大学）；IRCCS Humanitas Clinical and Research Hospital（IRCCS人道临床与研究医院）；CINI National Laboratory in Artificial Intelligence and Intelligent Systems（CINI人工智能与智能系统国家实验室）
Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs). However, in order to obtain good performance, these research achievements rely on hundreds of well-annotated WSIs. In this study, we tackle the tumor localization and detection problem under the setting of few labeled whole slide images and introduce a patch-based analysis pipeline based on the latest reverse knowledge distillation architecture. To address the extremely unbalanced n…
Queen’s University（女王大学）；University of British Columbia（不列颠哥伦比亚大学）；Exact Imaging（Exact Imaging公司）
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of supervised learning methods. On the other hand, …
Digitization of histopathology slides has led to several advances, from easy data sharing and collaborations to the development of digital diagnostic tools. Deep learning (DL) methods for classification and detection have shown great potential, but often require large amounts of training data that are hard to collect, and annotate. For many cancer types, the scarceness of data creates barriers for training DL models. One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming. DL…
Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Oral Pathology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院第九人民医院口腔病学部）；Department of Pathology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院新华医院病理科）；Department of Pediatric Hematology/Oncology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院新华医院儿童血液肿瘤科）；Clinical Research and Innovation Unit, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院新华医院临床研究与创新单元）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Breast Cancer Image Classification Method Based on Deep Transfer Learning
The Hong Kong University of Science and Technology（香港科技大学）；University of Southampton（南安普顿大学）；Trine University（特莱恩大学）；Southwest Jiaotong University（西南交通大学）
Northwestern University, Feinberg School of Medicine（西北大学范伯格医学院）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Mayo Clinic（妙佑医疗国际）
The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this article, we provide an overview of machine learning-based approaches that strengthen the varying a…
Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset
Johns Hopkins University（约翰斯·霍普金斯大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（马萨诸塞总医院）；Jagiellonian University（雅盖隆大学）；Stanford University（斯坦福大学）；Warmian-Masurian Cancer Center（瓦尔米亚-马祖里癌症中心）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Johns Hopkins Medicine（约翰斯·霍普金斯医学）
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University Hospital Erlangen（埃尔朗根大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Johannes Gutenberg-Universität Mainz（美因茨约翰内斯·古腾堡大学）；University Medical Center Mainz（美因茨大学医学中心）；Universität Regensburg（雷根斯堡大学）
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method. To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods w…
Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train convolutional neural networks (CNN) to classify image patches, but they are computationally expensive. In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection. Specif…
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection
University Center Salhi Ahmed（萨希·艾哈迈德大学中心）；University of Medea（迈代阿大学）；University of Dubai（迪拜大学）；Ecole Nationale Polytechnique（国立理工学院）；Texas A&M University（德克萨斯农工大学）；Northumbria University（诺森比亚大学）；Qatar University（卡塔尔大学）
This study focuses on automatic skin cancer detection using a Meta-learning approach for dermoscopic images. The aim of this study is to explore the benefits of the generalization of the knowledge extracted from non-medical data in the classification performance of medical data and the impact of the distribution shift problem within limited data by using a simple class and distribution balancer algorithm. In this study, a small sample of a combined dataset from 3 different sources was used to fine-tune a ResNet model pre-trained on non-medical data. The results show an increase in performance …
Throughout the world, breast cancer is one of the leading causes of female death. Recently, deep learning methods are developed to automatically grade breast cancer of histological slides. However, the performance of existing deep learning models is limited due to the lack of large annotated biomedical datasets. One promising way to relieve the annotating burden is to leverage the unannotated datasets to enhance the trained model. In this paper, we first apply active learning method in breast cancer grading, and propose a semi-supervised framework based on expectation maximization (EM) model. …
Eurecat, Centre Tecnològic de Catalunya（加泰罗尼亚技术中心 Eurecat）；Universitat Pompeu Fabra（庞培法布拉大学）；ICREA
The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules. In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung …
Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability
School of Systems Science and Industrial Engineering, Binghamton University（宾夕法尼亚大学系统科学与工业工程学院）
