The number of new cases, age-standardized rates and average age at diagnosis of cancers diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Cancer incidence rates are age-standardized using the direct method and the final 2011 Canadian postcensal population structure. Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

Analysis of ‘🎗️ Cancer Rates by U.S. State’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/cancer-rates-by-u-s-statee on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.

The rates are the numbers out of 100,000 people who developed or died from cancer each year.

Incidence Rates by State
The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.

• *Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

• ‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.

• †Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Death Rates by State
Rates of dying from cancer also vary from state to state.

• *Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

• †Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Source: https://www.cdc.gov/cancer/dcpc/data/state.htm

This dataset was created by Adam Helsinger and contains around 100 samples along with Range, Rate, technical information and other features such as: - Range - Rate - and more.

How to use this dataset

• Analyze Range in relation to Rate
• Study the influence of Range on Rate
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Helsinger

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

The Synthetic Colorectal Cancer Global Dataset is a fully anonymised, high-dimensional synthetic dataset designed for global cancer research, predictive modelling, and educational use. It encompasses demographic, clinical, lifestyle, genetic, and healthcare access factors relevant to colorectal cancer incidence, outcomes, and survivability.

Dataset Features

• Patient_ID: Unique identifier for each patient.
• Country: Patient's country of residence.
• Age: Age at diagnosis (in years).
• Gender: Biological sex of the patient (Male/Female/Other).
• Cancer_Stage: Stage of colorectal cancer at diagnosis (e.g., Stage I–IV).
• Tumor_Size_mm: Size of the tumor in millimeters.
• Family_History: Presence of colorectal cancer in family history (True/False).
• Smoking_History: Smoking behavior or history (e.g., Current, Former, Never).
• Alcohol_Consumption: Level of alcohol consumption (e.g., High, Moderate, None).
• Obesity_BMI: BMI classification related to obesity.
• Diet_Risk: Diet-related cancer risk (e.g., High Fat, Low Fiber).
• Physical_Activity: Level of physical activity (e.g., Sedentary, Active).
• Diabetes: Diabetes diagnosis (True/False).
• Inflammatory_Bowel_Disease: Presence of IBD (True/False).
• Genetic_Mutation: Genetic mutations relevant to colorectal cancer (e.g., APC, KRAS).
• Screening_History: History of cancer screenings (True/False).
• Early_Detection: Whether cancer was detected early (True/False).
• Treatment_Type: Primary treatment type (e.g., Surgery, Chemotherapy, Radiation).
• Survival_5_years: 5-year survival status (True/False).
• Mortality: Mortality outcome (Alive/Deceased).
• Healthcare_Costs: Estimated treatment costs (in USD).
• Incidence_Rate_per_100K: Country-level incidence rate per 100,000 people.
• Mortality_Rate_per_100K: Country-level mortality rate per 100,000 people.
• Urban_or_Rural: Patient's living area (Urban/Rural).
• Economic_Classification: Country's economic level (e.g., Low, Middle, High income).
• Healthcare_Access: Access level to healthcare services (e.g., Good, Limited).
• Insurance_Status: Insurance coverage status (Insured/Uninsured).
• Survival_Prediction: Model-derived survival prediction (probability or binary).

Distribution

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Usage

This dataset can be used for:

• Global Cancer Research: Analyze how clinical, lifestyle, and socioeconomic factors affect colorectal cancer outcomes worldwide.
• Predictive Modeling: Develop models to estimate survival probability or treatment outcomes.
• Healthcare Policy Analysis: Study disparities in healthcare access and outcomes across countries.
• Educational Use: Support training in epidemiology, oncology, public health, and machine learning.

Coverage

The dataset includes 100% synthetic yet clinically plausible records from diverse countries and demographic groups. It is anonymized and modeled to reflect real-world variability in risk factors, diagnosis stages, treatment, and survival without compromising patient privacy.

License

CC0 (Public Domain)

Who Can Use It

• Epidemiologists and Medical Researchers: To explore global patterns in colorectal cancer.
• Public Health Experts and Policymakers: For assessing equity in healthcare access and cancer outcomes.
• Data Scientists and Educators: As a rich dataset for teaching data analysis, classification, regression, and health informatics.

About Dataset 📌 Overview This dataset has been carefully synthesized to support research in lung cancer survival prediction, enabling the development of models that estimate:

Whether a patient is likely to survive at least one year post-diagnosis (Binary Classification). The probability of survival based on clinical and lifestyle factors (Regression Analysis). The dataset is designed for machine learning and deep learning applications in medical AI, oncology research, and predictive healthcare.

📜 Dataset Generation Process The dataset was generated using a combination of real-world epidemiological insights, medical literature, and statistical modeling. The feature distributions and relationships have been carefully modeled to reflect real-world clinical scenarios, ensuring biomedical validity.

📖 Medical References & Sources The dataset structure is based on well-established lung cancer risk factors and survival indicators documented in leading medical research and clinical guidelines:

World Health Organization (WHO) Reports on lung cancer epidemiology. National Cancer Institute (NCI) & American Cancer Society (ACS) guidelines on lung cancer risk factors and treatment outcomes. The IASLC Lung Cancer Staging Project (8th Edition): Standard reference for lung cancer staging. Harrison’s Principles of Internal Medicine (20th Edition): Provides an in-depth review of lung cancer diagnosis and treatment. Lung Cancer: Principles and Practice (2022, Oxford University Press): Clinical insights into lung cancer detection, treatment, and survival factors. 🔬 Features of the Dataset Each record in the dataset represents an individual’s clinical condition, lifestyle risk factors, and survival outcome. The dataset includes the following features:

1️⃣ Patient Demographics Age → A key risk factor for lung cancer progression and survival. Gender → Male and female lung cancer survival rates can differ. Residence → Urban vs. Rural (impact of environmental factors). 2️⃣ Risk Factors & Lifestyle Indicators These factors have been linked to lung cancer risk in epidemiological studies:

Smoking Status → (Current Smoker, Former Smoker, Never Smoked). Air Pollution Exposure → (Low, Moderate, High). Biomass Fuel Use → (Yes/No) – Associated with household air pollution. Factory Exposure → (Yes/No) – Industrial exposure increases lung cancer risk. Family History → (Yes/No) – Genetic predisposition to lung cancer. Diet Habit → (Vegetarian, Non-Vegetarian, Mixed) – Nutritional impact on cancer progression. 3️⃣ Symptoms (Primary Predictors) These are key clinical indicators associated with lung cancer detection and severity:

Hemoptysis (Coughing Blood) Chest Pain Fatigue & Weakness Chronic Cough Unexplained Weight Loss 4️⃣ Tumor Characteristics & Clinical Features Tumor Size (mm) → The size of the detected tumor. Histology Type → (Adenocarcinoma, Squamous Cell Carcinoma, Small Cell Carcinoma). Cancer Stage → (Stage I to Stage IV). 5️⃣ Treatment & Healthcare Facility Treatment Received → (Surgery, Chemotherapy, Radiation, Targeted Therapy). Hospital Type → (Private, Government, Medical College). 6️⃣ Target Variables (Predicted Outcomes) Survival (Binary) → 1 (Yes) if the patient survives at least 1 year, 0 (No) otherwise. Survival Probability (%) (Can be derived) → Estimated probability of survival within one year. ⚡ Why This Dataset is Valuable? ✅ Balanced Data Distribution Designed to ensure a representative distribution of lung cancer survival cases. Prevents model bias and improves generalization in predictive models. ✅ Medically-Inspired Feature Engineering Features are derived from real-world lung cancer risk factors, validated through medical literature. Incorporates both lifestyle and clinical indicators to enhance predictive accuracy.(no real person data is used,just have made an biomedical environment) ✅ Diverse Risk Factors Considered Smoking, air pollution, and genetic history as primary lung cancer contributors. Symptom severity and tumor histology influence survival rates. ✅ Scalability & ML Suitability Ideal for classification and regression tasks in machine learning. Can be used with deep learning (TensorFlow, PyTorch), ML models (XGBoost, Random Forest, SVM), and explainable AI techniques like SHAP and LIME. 📂 Dataset Usage & Applications This dataset is highly useful for multiple healthcare AI applications, including:

🩺 Predictive Analytics → Early detection of high-risk lung cancer patients. 🤖 Healthcare Chatbots → AI-powered risk assessment tools.

Annual percent change and average annual percent change in age-standardized cancer incidence rates since 1984 to the most recent diagnosis year. The table includes a selection of commonly diagnosed invasive cancers, as well as in situ bladder cancer. Cases are defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3) from 1992 to the most recent data year and on the International Classification of Diseases, ninth revision (ICD-9) from 1984 to 1991.

India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 19.800 NA in 2016. This records a decrease from the previous number of 20.000 NA for 2015. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 21.200 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.400 NA in 2000 and a record low of 19.800 NA in 2016. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Analysis of ‘Breast Cancer Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/breast-cancer-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Description:

Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases, and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area.

The key challenges against it’s detection is how to classify tumors into malignant (cancerous) or benign(non cancerous). We ask you to complete the analysis of classifying these tumors using machine learning (with SVMs) and the Breast Cancer Wisconsin (Diagnostic) Dataset.

Acknowledgements:

This dataset has been referred from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification models to predict whether the cancer type is Malignant or Benign.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

--- Original source retains full ownership of the source dataset ---

Cervical cancer is one of the leading causes of cancer-related deaths among women worldwide. Early detection and accurate prediction of cervical cancer can significantly improve the chances of successful treatment and save lives. This dataset help to develop a predictive model using machine learning techniques to identify individuals at high risk of cervical cancer, allowing for timely intervention and medical care.

Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple machine learning models on a numerical dataset of lung cancer-relevant parameters and compares performance and accuracy. After comparison, each model has been explained using different methods. The main contribution of this research is to give logical explanations of why the model reached a particular decision to achieve trust. This research has also been compared with a previous study that worked with a similar dataset and took expert opinions regarding their proposed model. We also showed that our research achieved better results than their proposed model and specialist opinion using hyperparameter tuning, having an improved accuracy of almost 100% in all four models.

BY: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 23.800 % in 2021. This records a decrease from the previous number of 24.600 % for 2020. BY: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 31.100 % from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 36.100 % in 2002 and a record low of 23.800 % in 2021. BY: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/].

LT: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 12.400 NA in 2016. This records a decrease from the previous number of 13.300 NA for 2015. LT: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 14.100 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 15.500 NA in 2005 and a record low of 12.400 NA in 2016. LT: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lithuania – Table LT.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 20.900 NA in 2016. This records an increase from the previous number of 20.800 NA for 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 21.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 22.600 NA in 2000 and a record low of 20.800 NA in 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 13.400 % in 2016. This records an increase from the previous number of 13.300 % for 2015. Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 13.400 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 17.300 % in 2000 and a record low of 13.300 % in 2015. Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 16.400 % in 2016. This records a decrease from the previous number of 16.500 % for 2015. Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 17.900 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.900 % in 2000 and a record low of 16.400 % in 2016. Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcancer-auto-segmentation.

BackgroundWe aim to evaluate the global, regional, and national burden of Uterine Cancer (UC) from 1990 to 2019.MethodsWe gathered UC data across 204 countries and regions for the period 1990-2019, utilizing the Global Burden of Disease Database (GBD) 2019 public dataset. Joinpoint regression analysis was employed to pinpoint the year of the most significant changes in global trends. To project the UC trajectory from 2020 to 2044, we applied the Nordpred analysis, extrapolating based on the average trend observed in the data. Furthermore, the Bayesian Age-Period-Cohort (BAPC) model with integrated nested Laplace approximations was implemented to confirm the stability of the Nordpred analysis predictions.ResultsGlobally, the age-standardized rate (ASR) of incidence for UC has increased from 1990 to 2019 with an Average Annual Percentage Change (AAPC) of 0.50%. The ASR for death has declined within the same period (AAPC: -0.8%). An increase in the ASR of incidence was observed across all Socio-demographic Index (SDI) regions, particularly in High SDI regions (AAPC: 1.12%), while the ASR for death decreased in all but the Low SDI regions. Over the past 30 years, the highest incidence rate was observed in individuals aged 55-59 (AAPC: 0.76%). Among 204 countries and regions, there was an increase in the ASR of incidence in 165 countries and an increase in the ASR of deaths in 77 countries. Our projections suggest that both the incidence and death rates for UC are likely to continue their decline from 2020 to 2044.ConclusionsUC has significantly impacted global health negatively, with its influence stemming from a range of factors including geographical location, age-related and racial disparities, and SDI.

This dataset contains counts of deaths for California residents by ZIP Code based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths of California residents. The data tables include deaths of residents of California by ZIP Code of residence (by residence). The data are reported as totals, as well as stratified by age and gender. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcanc-ultrasound-class.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcancer-auto-objdetect.

Automatic detection of diseases by use of computers is an important, but still unexplored field of research. Such innovations may improve medical practice and refine health care systems all over the world. However, datasets containing medical images are hardly available, making reproducibility and comparison of approaches almost impossible. Here, we present Kvasir, a dataset containing images from inside the gastrointestinal (GI) tract. The collection of images are classified into three important anatomical landmarks and three clinically significant findings. In addition, it contains two categories of images related to endoscopic polyp removal. Sorting and annotation of the dataset is performed by medical doctors (ex- perienced endoscopists). In this respect, Kvasir is important for research on both single- and multi-disease computer aided detec- tion. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.

Data Collection The data is collected using endoscopic equipment at Vestre Viken Health Trust (VV) in Norway. The VV consists of 4 hospitals and provides health care to 470.000 people. One of these hospitals (the Bærum Hospital) has a large gastroenterology department from where training data have been collected and will be provided, making the dataset larger in the future. Furthermore, the images are carefully annotated by one or more medical experts from VV and the Cancer Registry of Norway (CRN). The CRN provides new knowledge about cancer through research on cancer. It is part of South-Eastern Norway Regional Health Authority and is organized as an independent institution under Oslo University Hospital Trust. CRN is responsible for the national cancer screening programmes with the goal to prevent cancer death by discovering cancers or pre-cancerous lesions as early as possible.

Dataset Details The Kvasir dataset consists of images, annotated and verified by medical doctors (experienced endoscopists), including several classes showing anatomical landmarks, phatological findings or endoscopic procedures in the GI tract, i.e., hundreds of images for each class. The number of images is sufficient to be used for different tasks, e.g., image retrieval, machine learning, deep learning and transfer learning, etc. The anatomical landmarks include Z-line, pylorus, cecum, etc., while the pathological finding includes esophagitis, polyps, ulcerative colitis, etc. In addition, we provide several set of images related to removal of lesions, e.g., "dyed and lifted polyp", the "dyed resection margins", etc. The dataset consist of the images with different resolution from 720x576 up to 1920x1072 pixels and organized in a way where they are sorted in separate folders named accordingly to the content. Some of the included classes of images have a green picture in picture illustrating the position and configuration of the endoscope inside the bowel, by use of an electromagnetic imaging system (ScopeGuide, Olympus Europe) that may support the interpretation of the image. This type of information may be important for later investigations (thus included), but must be handled with care for the detection of the endoscopic findings.

Data Source You can find data on kvasir

Segmentation Data Kvasir-SEG Dataset Details The Kvasir-SEG dataset (size 46.2 MB) contains 1000 polyp images and their corresponding ground truth from the Kvasir Dataset v2. The resolution of the images contained in Kvasir-SEG varies from 332x487 to 1920x1072 pixels. The images and its corresponding masks are stored in two separate folders with the same filename. The image files are encoded using JPEG compression, and online browsing is facilitated. The open-access dataset can be easily downloaded for research and educational purposes.

The bounding box (coordinate points) for the corresponding images are stored in a JSON file. This dataset is designed to push the state of the art solution for the polyp detection task. Some examples of the dataset.

Data Source You can find data on SegKvasir