: Machine learning for medical image scanning I'm searching for a topic of interest in the domain of machine learning and computer vision. More specifically, researching can computer vision be applied to classify medical image

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Commonly used diagnostic imaging methods are:

Ultrasonography
X-ray
Computed tomography (CT)
Magnetic resonance imaging (MRI)
Scintigraphy or radionuclide scan (injecting a radioactive tracer into a vein, waiting until it collects in a certain organ, for example, the thyroid gland, and taking a picture of the tracer distribution with a scanner)

All the mentioned techniques have several variants, for example Doppler ultrasound, an MRI with contrast, etc. The Wikipedia's Medical Imaging has a more detailed "index" of techiques with links to individual articles.

On Biology SE, there's a list of websites that provide open-access images, some of which come with the descriptions of cases. Before buying any book, I strongly recommend you to get a clear idea about which types of books can serve your purpose. A book that can be excellent for a doctor or medical student can be useless for you. I also recommend that, for a start, you chose ONE imaging technique and research it a bit, rather than go with all imaging at once; the problems in ultrasonography are significantly different than in CT.

Examples of weaknesses of imaging techniques:

A CT and MRI, at least, are expensive.
An X-ray can show only lesions that are significantly more or less radiopaque than the surrounding tissues (for example, it can show only gallstones rich in calcium but not others).
An MRI of the gallbladder cannot reliably distinguish between the noncancerous polyps and cancers (Radio Graphics).
The most common problem is probably, that despite high sensitivity (ability to detect a lesion), specificity (ability to reveal/predict an exact type of the lesion) of CT and MRI scans can still be low.

A common question for a health practitioner that often remains unresolved after imaging is: Is the lesion cancerous or not or how likely will it develop into a cancer. For example, gallbladder polyps greater than 10 mm are significantly more likely cancerous than the smaller ones, but it is not clear if the risk increases after 5 mm or after 15 mm, for example. Also, sometimes imaging fails to show if cancer has spread to nearby organs.

EDIT:

To predict a lesion on a CT image, you need to know how a normal CT image looks and how a lesion looks. The knowledge about what is a lesion came from comparisons of many CT images and the actual physical situations discovered during surgery. Now, to extend this knowledge beyond what you can see with your own eyes on a CT image, you would again need to compare many CT images (using a computer program) with surgery results.

I imagine, this would require a project in which several experienced radiologists, surgeons and computer experts would be involved. One project would need to focus on a single question, for example: What are predictors of gallbladder cancer in abnormal gallbladder growths detected on a CT image? Thousands of CT images and surgery results would then need to be compared to find eventual associations.

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I would like to make a recommendation, as a researcher also working in medical imaging. You state that you are interested in predicting lesion diameter, patient gender, and patient age from the scans. However, when a radiologist reads a scan, they already know the patient gender and the patient age because that information is in the medical record. They also know the reason for the scan. For example, they will often see a display like "Ms. Smith is a 55-year old woman with a history of lung cancer" along with the scan itself. (And they can click on the patient's medical record and view everything in the medical record if they want.) So, I think you are better off not predicting things that are already known to the doctor. There are many other cool medical imaging tasks you can do with the DeepLesion data set, e.g. like predicting if there is a lesion in the scan.

Here are some other resources that might help you:

Overview of basic chest anatomy for radiology and abdomen anatomy for radiology
Radiology terms of location
How to read normal chest x-rays. Chest x-rays are not CTs, but if you are just learning about medical imaging it is easier to start with chest x-rays and then move on to CTs
Interpretation of abdominal CTs