Masoom Haider

Prostate MRI academic radiologist

[Masoom Haider @radfiler]

@eb_radnucs For sure. So far in the prostate arena evidence of this happening with AI is still weak but I think the field will get there. Hopefully something soon.

[Masoom Haider @radfiler]

A DICOM standard has been developed to report common data elements such as PSA, ethnicity, age, etc in prostate MRI image metadata. This is one approach could provide a universal standard for unbiased data collection #AJRChat

[Masoom Haider @radfiler]

Q5: Developing expertise in med imaging in this field including diversity, inclusion... Collecting standard data on all cases that are potential confounders such as known risk profile information and reporting it... #AJRChat

[Masoom Haider @radfiler]

Q4: The ACR DSI @AcrDsi is taking some initiatives to provide platforms for validation which should help move the field forward #AJRChat

[Masoom Haider @radfiler]

Q4 Agree 100% with Baris. In my view use of public data sets, the need for open source code and evidence of stability after deployment are not emphasized enough in CLAIM for those in clinical practice. #AJRChat

[Masoom Haider @radfiler]

Q3 Quality assurance is definitely up there. There is promising work coming from the manufacturers and places like NIH with Baris @radiolobt and elsewhere for PIRADS classification which is one of the "holy grails" #AJRChat

[Masoom Haider @radfiler]

Q3: We can speculate on things to hit us first in prostate, my vote is for noise reduction with pulse sequences (DWI) and prostate volumetrics. Others still need work but are likely to come. #AJRChat

[Masoom Haider @radfiler]

Q3: Categories: At the scanner-noise reduction, quality assurance; At the workstation: gathering history from EPR, cancer localization/segmentation, report generation #AJRChat

[Masoom Haider @radfiler]

Q2: Menial task examples: prostate volume, change in prostate vol over time. Many others outside prostate domain. Co-registration for detecting changes over time #AJRChat

[Masoom Haider @radfiler]

Q2: Failure: A prostate cancer DL semantic segmentation tool that completely missing a cancer that fills the whole gland on MRI #AJRChat

[Masoom Haider @radfiler]

Q2 - Advantage DL does not fatigue. Measuring prostate volumes accurately is a concrete example of a DL assisted functionality we could use for PSA density #AJRChat

[Masoom Haider @radfiler]

Q2: You wouldn't leave a 2 year old alone in the house. Human supervision required #AJRChat

[Masoom Haider @radfiler]

Q2: Independent DL without human supervision in many domains is still difficult. When DL fails it often does so catastrophically. #AJRChat

[Masoom Haider @radfiler]

Q2: DL has as yet unrealized potential. Advantage - it has the potential to free us from menial task, improve our efficency, augment us #AJRChat

[Masoom Haider @radfiler]

Q1: Machine Learning is an algorithmic approach where the algorithm learns and adapts from drawing inferences from data. It is data driven and encompasses many approaches #AJRChat

[Masoom Haider @radfiler]

Q1: Typically deep means many layers with most successful networks have 16 or more with 50 not being unusual #AJRChat

[Masoom Haider @radfiler]

Deep Learning is a type of ML. It is based on artificial neural networks. It has an input layer and an output layer. The deep part refers to having many intermediate layers. #AJRChat

[Masoom Haider @radfiler]

Hi. Glad to be with you today #AJRChat

[Masoom Haider @radfiler]

@radiolobt Glad you liked this Baris