Reduces The Chance Of Needing A Mastectomy Or Chemotherapy
A mastectomy is the full removal of a breast. Sometimes, a double mastectomy is needed, meaning that both breasts are entirely removed. If the cancer is detected early enough, a mastectomy may not be necessary.
This same idea applies to chemotherapy, as well. The earlier the cancer is detected, the less invasive the treatment plan will have to be. Caught early enough, chemotherapy may not be necessary at all.
How To Do A Breast Self
Step 1: Begin by looking at your breasts in the mirror with your shoulders straight and your arms on your hips.
Here’s what you should look for:
If you see any of the following changes, bring them to your doctor’s attention:
|Breast Self-Exam Step 1|
The Ai Tool For Breast Cancer Detection Explained:
The AI developed by Google analyses X-ray images know as mammograms and reduces the number of false negatives by 9.4 percent and false positives by 5.7 percent for women in the US. While for women in the UK it cuts 2.7 percent in false negatives and 1.2 percent in false positives.
Although this system outperformed doctors in most cases, there were other cases where doctors flagged breast cancer that was missed by the AI model.
This new AI Tool developed by Google is just one of many new technological developments within the field of Computer-vision. This field has seen major improvements in the latest years. Within the last 10 years, algorithms are much more capable of detecting objects and analyzing large visual datasets. The field of Deep learning brought what is known as Neural Networks as a way to analyze large and complex datasets. Furthermore, Convolutional Neural Networks is what has made a huge revolution in the field of Computer Vision.
In addition to Google AI being able to detect breast cancer more accurately than doctors, CNNs allow many other applications such as Face recognition, Surveillance, Biometrics, and Autonomous Driving.
Computer-Vision problems are mostly tasks such as localization, image classification, and object detection.
According to the research paper, this is the structure of the AI:
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Different Types Of Mammograms
The conventional method involves taking images in four views of the breast. Patients are often called back for additional testing when using this method of screening, and cancer may sometimes be confused for dense or overlapping tissue. This method is sometimes paired with an ultrasound of the breast, as well.
Also known as a 3D-mammogram or a tomo-mammogram, this is the best method because it bases the number of slices on the size of the breast. This allows for more accurate and thorough screenings for those with larger breasts. The average is 40 slices, giving radiologists a better view than in the other method and lowering the chance that a patient will have to return for more tests.
Unfortunately, tomo-mammograms arent offered at all mammography centers, and they are more expensive than conventional screening. It is important to note, however, that if you have to go back for a second traditional screening, you will end up paying more than you would have for a 3D-mammogram.
If something is founding during your first screening, you will be called back for another one, which is called diagnostic mammography. During this, more X-rays will be taken to get images from different angles and get a more in-depth look at the mass or calcium area.
Converting A Classifier From Recognizing Patches To Whole Images
Converting a patch classifier to an end-to-end trainable whole image classifier using an all convolutional design. The function f was first trained on patches and then refined on whole images. We evaluated whether removing the heatmap improved information flow from the bottom layers of the patch classifier to the top convolutional layers in the whole image classifier. The magnifying glass shows an enlarged version of the heatmap. This figure is best viewed in color.
The function h accepts whole images as input and produces labels at the whole image level. Therefore, it is end-to-end trainable, providing two advantages over the two-step approach. First, the entire network can be jointly trained, avoiding sub-optimal solutions from each step Second, the trained network can be transferred to another dataset without explicit reliance on ROI annotations. Large mammography databases with ROI annotations are rare and expensive to obtain. The largest public database with ROI annotations for digitized film mammograms DDSM contains several thousand images with pixel-level annotations, which can be exploited to train a patch classifier f. Once the patch classifier is converted into a whole image classifier h, it can be fine-tuned on other databases using only image-level labels. This approach allows us to significantly reduce the requirement for ROI annotations, and has many applications in medical imaging in addition to breast cancer detection on screening mammograms.
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Clinical Breast Exams And Regular Breast Self
Routine examination of the breasts by health care providers or by women themselves has not been shown to reduce deaths from breast cancer. However, if a woman or her health care provider notices a lump or other unusual change in the breast, it is important to get it checked out. For more information, see the PDQ® Breast Cancer Screening summary.
Creating A Neural Network Model
In addition to the aforementioned diagnostic models, a Neural Network model was created and tuned using the architecture shown below.
Neural Network model architecture.
This neural network classifier has resulted in 0.97 F1 mean scores on cross-validation. This new neural network models F1 score is better compared to the best models score gained in Step 3. Here are the top three models results so far.
Cross-validation results of the top three models.
Now lets evaluate these models on the test data set which previously was not shown to classifiers imitating new data. Below are the results demonstrating just how well these models performed on the test data set.
As its shown in the graph, neural network classifier have performed better by gaining 0.97 F1 scores on the test set.
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Other Screening Tests Have Been Or Are Being Studied In Clinical Trials
Studies have been done to find out if the following breast cancer screening tests are useful in finding breast cancer or helping women with breast cancer live longer.
A clinical breast exam is an exam of the breast by a doctor or other health professional. He or she will carefully feel the breasts and under the arms for lumps or anything else that seems unusual. It is not known if having clinical breast exams decreases the chance of dying from breast cancer.
Breast self-exams may be done by women or men to check their breasts for lumps or other changes. If you feel any lumps or notice any other changes in your breasts, talk to your doctor. Doing regular breast self-exams has not been shown to decrease the chance of dying from breast cancer.
Thermography is a procedure in which a special camera that senses heat is used to record the temperature of the skin that covers the breasts. Tumors can cause temperature changes that may show up on the thermogram.
There have been no randomized clinical trials of thermography to find out how well it detects breast cancer or the harms of the procedure.
Breast tissue sampling is taking cells from breast tissue to check under a microscope.Breast tissue sampling as a screening test has not been shown to decrease the risk of dying from breast cancer.
The Potential Benefits Of Ai For Breast Cancer Detection
One of the best ways to fight cancer is early detection, when it is still confined and can be fully excised surgically or treated pharmacologically. Cancer screening programs, that is, the practice of testing for the presence of cancer in people who have no symptoms, has been medicines tool of choice for the earliest detection.
Figure 1. Out of 1000 woman going for a screening mammogram, 100 will be recalled for further studies, but only 5 of them will actually have cancer. This represents a 9.5% of false positives
In this day and age in which we see the remarkable success of applying deep learning to speech recognition, visual object detection and recognition and natural language processing to name a few, it is natural to ask whether deep learning can help improve radiologists interpretation of mammograms. At IBM Research, we are working hard to apply deep learning to create technology which could support radiologists as they work to detect breast cancer at screening4 an effort that many others across AI and health communities are also focused on5.
We believe AI could help improve the scalability of breast cancer screening and ameliorate the shortage of mammography professionals around the world. In a recent paper published on March 2nd, 2020 in JAMA Network Open6 we have shown progress is being made in this direction.
Model-to-data practices protect sensitive datasets in crowdsourced competitions
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Getting A Second Opinion
Getting a second opinion during your cancer care process is common. Its a good idea to get your second opinion before starting treatment, because a second opinion can alter your diagnosis and thus your treatment. However, you can get a second opinion at any point during treatment.
During your cancer care, consider asking for a second opinion in these instances:
- after your pathology report is complete
- before surgery
- after your staging work is complete, if you are uncomfortable with the treatment plan your doctor recommends
- while planning treatments following surgery
- during treatment, if you believe there may be a reason to change the course of your treatment
- after completing treatment, especially if you didnt ask for a second opinion prior to starting treatment
Best Scans To Detect Cancer
Early detection and diagnosis of cancer can significantly increase your chances of being treated successfully. Early cancer treatments can also be less expensive and complicated than more advanced disease treatments, which may spare you and your family heartaches.
Early detection also helps doctors identify precancerous tissue abnormalities destined to become life-threatening cancers, which provides the chance for earlier intervention for preventing cancer from ever developing at all. Similarly, being able to identify precancerous tissue abnormalities accurately, and early cancers before they turn into possibly fatal malignancies, can also spare you and your family from the potential financial and physical burdens of unnecessary treatment.
Still, too many individuals are receiving cancer diagnoses at late stages, jeopardizing their chances of successful treatment and long-term survival. Imaging in cancer diagnosis can be a beneficial tool to detect malignancies at their earliest stages to increase your treatment success.
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Mammography Is Not For All Women
Mammography for breast cancer is not for women who are already going through a severe illness that significantly reduces their life expectancy. Womens overall health is a priority when youre planning to get screening mammography. There is no point in searching for another disease when they are already suffering from a life-threatening illness.
As per the American Cancer Society, women who have severe illness should consult with their physician to get mammography screening. Apart from it, mammograms are useless for patients whose cancer has spread already. Your doctor will make the final call on what should be done!
Exposure to RadiationAlthough mammography for breast cancer uses low radiation, getting more than one mammography can increase the risk.
Overtreatment and OverdiagnosisAnother possible limitation of mammography for breast cancer is overdiagnosis and overtreatment. Mammography is expected to point out ductal carcinoma or invasive breast cancer, but such cancers are not likely to grow or spread. In such cases, looking for a treatment to cure the disease refers to overdiagnosis. Although they are harmless, the doctor cannot be particular if the disease will spread or not.
Mammograms Show Patterns In Calcium Calcifications In The Breast
In other methods for detecting cancer in the breast, these areas of calcium do not show up as well, and the patters are indistinguishable. During this type of test, however, doctors can read the patterns of calcium areas, which allows them to see if the area is likely to become breast cancer or if it is due to non-harmful changes within the body.
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Who Is At Risk For Developing Breast Cancer
Anyone can develop this cancer. Indeed, 75 percent of women who receive a breast cancer diagnosis have no family history of the disease. Doctors dont consider these women high risk, either because of their clean family history.
Anyone who is considered high-risk must take extra precautions. Thats because their likelihood of developing cancer outweighs the risks of others. Those who are regarded as high risk include:
- Those with a history of breast cancer in their family
- Women who are 50 years of age or older
While this disease is thought to only occur in women, men have a small chance of developing cancer, as well. Doctors may not recommend regular breast screenings for them, but they should still do self-exams at home and tell their doctor if they feel any changes or other lumps.
What Are Imaging Tests And How Can They Detect Cancer
Imaging tests help doctors take a look at whats occurring inside your body, and can also show the doctor how far your cancer has spread, and whether the treatment is working.
Imaging tests work by sending types of energy like sound waves, x-rays, magnetic fields or radioactive particles through your body. The tissues of your body then change these energy patterns to create a picture or image.
Doctors use imaging tests for cancer in various ways, including:
- Identifying cancer in its earliest stages when its small and hasnt spread, and youre not experiencing symptoms.
- Looking for a lump or tumor if you are experiencing symptoms. They can also see if its cancer or another disease causing your symptoms.
- Predicting if your tumor is potentially cancerous and whether the doctor needs to remove and analyze a small tissue sample to help determine this. Usually, doctors can use the biopsy process to see if any change is cancer.
- Seeing where your tumor sits, even if its deep inside your body.
- Finding out what stage your cancer is in, or if it has spread to other parts of your body.
- Planning treatment, such as showing where the doctor needs to target cancer radiology therapy beams.
- Detecting if your tumor has grown, shrunk or stayed the same following treatment. This lets the doctor know how well your treatment is working, and whether any changes in therapy are necessary.
- Identifying if your cancer returned after treatment.
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Challenges Of Applying Machine Learning In Healthcare
There are several obstacles impeding faster integration of machine learning in healthcare today. One of the biggest challenges is the ability to obtain patient data sets which have the necessary size and quality of samples needed to train state-of-the-art machine learning models. Since patient data is protected by strict privacy and security rules, the data is not easy to collect, share and distribute. Furthermore, there are challenges with the format and quality of data which usually require significant effort to clean and prepare for machine learning analyses.
As machine learning and data science are starting to be adopted as a tool in healthcare applications, the industry is slowly pushing the boundaries on what it can do. Its primary function will most likely involve data analysis based on the fact that each patient generates large volumes of health data such as X-ray results, vaccinations, blood samples, vital signs, DNA sequences, current medications, other past medical history, and much more.
Using Machine Learning To Detect And Diagnose Breast Cancer
One application of machine learning in a healthcare context is digital diagnosis. ML can detect patterns of certain diseases within patient electronic healthcare records and inform clinicians of any anomalies. In this sense, the artificial intelligence technique can be compared to a second pair of eyes that can evaluate patient health based on the knowledge extracted from big data sets by summarizing millions of observations of diseases that a patient could possibly have. To illustrate just how useful machine learning as a medical diagnosis tool can be, I examined its use in breast cancer detection using a publicly available Breast Cancer Wisconsin Data Set.
This data set consists of several instances of tumors. Tumors can either be benign or malignant . Benign tumors grow locally and do not spread. As a result, they are not considered cancerous. However, they can still pose a danger, especially if they press against vital organs like the brain. Malignant tumors, in contrast, have the ability to spread and invade other tissues. This process, known as metastasis, is a key feature of cancer. There are many different types of malignancy-based tumors as well as locations that this type of cancer tumor can originate, as described in the data set specification.
The breast cancer data set consists of 699 tumor samples where 458 are benign tumors and 241 malignant tumors. Instances in the data set have the following attributes:
Nuclear Medicine Scans For Cancer
Nuclear medicine scans generate images based on your body chemistry, instead of on physical forms and shapes, as with other imaging tests. The scans use liquid substances known as radionuclides, radiopharmaceuticals or tracers that release low radiation levels.
Body tissues affected by some diseases, like cancer, might absorb more or less of the tracer than normal tissues. Specific cameras pick the radioactivity pattern up to generate images that show the doctor where the tracer travels as well as where it collects.
The tumor might show up on the image as a hot spot if cancer is present. The hot spot is a spot of increased tracer uptake and cell activity. The tumor may also be a cold spot, depending on what type of scan the doctor performs. The cold spot is the area of less cell activity or decreased uptake.
The doctor will determine the type of nuclear scan youll receive, depending on which organ they wish to look into. Several common nuclear scans used for cancer include:
- PET scans
- Multigated acquisition scans
- Thyroid scans
Nuclear scans might not identify tiny tumors, and cant always indicate if a tumor is cancer.
Benefits of Nuclear Medicine Scans
Nuclear medicine scans help doctors identify tumors, and determine the extent the cancer has spread throughout your body. They also help doctors learn if a treatment is working. Theyre relatively painless tests typically performed on an outpatient basis.