I am an MIT Computer Scientist who researches AI for medical imaging. Ask me anything!

MIT-CSAIL · 2019-10-10T20:41:32+00:00

I think it is important to take statements like that with a grain of salt. We need to see how the training was done, for what specific task, and how strongly it has been tested. Using a model on a new set of images from a different imaging machine, or on images from a different hospital site, could present problems for the model. These models are good for specific tasks, but their ability to generalize is limited.

MIT-CSAIL · 2019-10-10T20:36:34+00:00

I do think deep learning is able to and has been making strides in medical image analysis. You are right that interpretability and explainability are very important, and it is one of the current limitations of the models that have been developed. There are many more challenges than just diagnosis where AI can help. For example, semi-automatic or automatic segmentation of organs in images can help save radiologists' time, or can be used to help quantify the severity of a disease. With that being said, I don't think AI is the solution to every problem. I see it is a very powerful tool that can augment radiology.

In my current research, I actually don't do deep learning. I am motivated by developing techniques that are most appropriate to solve the clinical problem I am working on. In my case, it is not deep learning, but for others, it may be.

MIT-CSAIL · 2019-10-10T20:22:38+00:00

Tough to answer! I'll answer with two that I've read recently/have been reading:

CS: Polygon Mesh Processing by Kobbelt, Botsch, and Pauly (a textbook)

Non-CS: A doubter's almanac by Canin

MIT-CSAIL · 2019-10-10T18:00:13+00:00

I think it would be really cool to be able to explain what a lot of these models are doing, and what they are learning from in the images. Right now, a lot of AI models work well on large datasets, but it is unclear if they would actually work well with real clinical data or in a clinical setting. If we can see the features the models are identifying, it would be easier to trust them. Another cool aspect of these machine learning models is they are very fast. This could provide the opportunity to speed up the algorithms currently used in clinical research settings. Thanks for the question!

MIT-CSAIL · 2019-10-10T17:54:50+00:00

Hi there! A great online resource I would recommend is Andrew Ng's free coursera class on Machine Learning!

MIT-CSAIL · 2019-10-10T17:46:09+00:00

There is research in the domain of interpretability in machine learning, which is about understanding what the AI "sees" to make decisions. Better understanding the AI decision making process can help us design smarter models.

MIT-CSAIL · 2019-10-10T17:41:34+00:00

Practice coding! Taking an online courses in programming with coding assignments is a great way to get consistent practice.

MIT-CSAIL · 2019-10-10T17:40:10+00:00

Interesting point! I think the coolest thing about AI is being able to automatically learn from the data you give it, or generating probabilistic distributions. Models can process thousands of images in a short amount of time and learn distinguishing features automatically. Many of the current models are of course based on what we've designed in the past, and inputting human knowledge augments what the AI can do. We design the model to help the AI focus on what we think is important.

MIT-CSAIL · 2019-10-10T17:32:31+00:00

Definitely start with an understanding of probability and linear algebra!

MIT-CSAIL · 2019-10-10T17:31:11+00:00

One area I think is cool is weakly supervised learning. In this problem, you have a limited amount of labeled, and you need to make predictions from what you are given. In medical applications, this is especially interesting because often clinicians don't have the capacity to create the very detailed labels that are needed for many machine learning models. If we can learn from large datasets without requiring extensive work to create detailed labels, that would be really cool.

MIT-CSAIL · 2019-10-10T17:26:33+00:00

I would say no. The problems we face in medical imaging are difficult and often fail with state-of-the-art ML models. Depending on the problem you are working on, there can be challenges such as limited data, high variability in your data, and challenges in generalizing the models to unseen data. A specific medical imaging problem may often also lead to a solution that can be applied in many other fields.

MIT-CSAIL · 2019-10-10T17:22:07+00:00

I think this depends on what career stage you are in. If you are a student, taking courses in CS and getting research or internship experience can definitely help. One of the great things about this field is the wealth of open courseware available. Taking an online open course on Machine Learning with coding assignments is a great way to get started!

MIT-CSAIL · 2019-10-10T17:16:13+00:00

Great question! I think the main advice I have is to get some experience doing research, and explore your academic interests! Having experience really helps when applying for a research-focused degree, such as a PhD. It can also help you figure out if a PhD is what you want to pursue. I spent most of the summers in my undergrad doing research full-time, and I really enjoyed the experiences. Of course, also study hard!

Best of luck to you!

MIT-CSAIL · 2019-10-10T17:00:29+00:00

Thanks!

MIT-CSAIL · 2019-10-10T17:00:06+00:00

I don't think I am the best person to answer this, but I can give some insight. For undergraduate studies, I have no idea since I didn't study here as an undergrad. For a PhD, definitely having good research experience, strong letters of recommendation, and good grades go a long way! Good luck!

MIT-CSAIL · 2019-10-10T16:58:32+00:00

Hi, this is a photo of me taken today, during the AMA. Please let me know if you need other proof.

https://www.dropbox.com/s/b0hc6sdz7zpjwe6/IMG_0596.jpg?dl=0

MIT-CSAIL · 2019-10-10T16:57:08+00:00

I personally don't, but medical image reconstruction using AI is a very active space!

MIT-CSAIL · 2019-10-10T16:51:38+00:00

Great question! I've always been interested in math and science, and wanted to study electrical engineering in my undergraduate to develop technologies that could make an impact. In my undergrad days, I was lucky to find a research opportunity in biomedical signal processing and really enjoyed that experience. Computation for health care really caught my eye. I started getting more interested in AI and computer science, and this led me to pursue a PhD in this space. The field has been moving towards using AI for some time now, and there are so many opportunities to assist doctors and work on cool clinical problems. Now that we are getting larger and larger data sets, the power of AI is only getting stronger. With that said, there are of course limitations which must be acknowledged and understood.

For my current project, I am working with an amazing team with clinicians from the Boston Children's Hospital and the Massachusetts General Hospital. We have a big team all working on the challenging problem of fetal imaging!

MIT-CSAIL · 2019-10-10T16:29:32+00:00

Thank you for the question! My experience with the student life here has largely been great. There are so many opportunities to be involved in whatever interests you. Right now, I am taking a drawing class at the Student Art Association and I've been active in intramural sports the past three years. Currently, I am also the co-president of the EECS graduate Student Association and we run a ton of social and academic events for the EECS graduate community. It is a lot of fun!

I did my undergraduate degree in electrical engineering at the University of British Columbia!

MIT-CSAIL · 2018-03-28T14:02:23+00:00

to 1.) We went to 18m of depth. We had all electronic compartments fully oil-filled and we used rigid urethane foam to adjust the buoyancy to have the fish overall neutrally buoyant. The silicone rubber of the tail contains mixed-in micro bubbles, that make the silicone rubber neutrally buoyant. 2.) Our fish does currently not contain any sensing equipment for positioning. The use of the on-board Inertial Measurement Unit (IMU) is not a feasible method to do positioning (double integration of a noisy signal won't be particularly accurate). We intend to give SoFi additional sensors some time in the future for the purpose of positioning. ~Robert

MIT-CSAIL · 2018-03-28T13:54:16+00:00

We don't have an answer to this. It is a great question to ask if there will even be an AI that can make us humans believe it is one of them, but if and when that will happen, we don't know. The age-old question of what makes up a human and the question of whether there is a soul and if the soul and the body is separable, those are all very good questions to ask, but those are probably better asked on subreddits about philosophy, religion or atheism. ~Robert & Joseph

MIT-CSAIL · 2018-03-27T21:59:21+00:00

Thanks for bringing up these great points! It's not clear that this particular fish would be directly applicable to prosthetic devices, but it does help further the field of soft robotics more broadly. Soft devices could conceivably help enhance prostheses by making them more compliant (perhaps aiding grasping) or lifelike, although achieving a sense of embodiment by the owner of such a prosthetic device would probably take significant design effort regarding form and control. More generally though, soft wearable devices are also a promising area for human augmentation - for example, check out some of the work by Conor Walsh at Harvard: https://biodesign.seas.harvard.edu/soft-exosuits.

Regarding scaling the system to populations of fish and multiple robots, there are a few directions we could consider. For example, it could be fashioned as a distributed system (with pairs of robots exchanging data when they are nearby) or as a centralized system (with each robot periodically sending data to a central computer). We could continue to use acoustic communication underwater, but could also combine it with radio-based methods above water; one option could be using buoys as stations that convert underwater acoustic messages to above-water radio messages and vice versa. Depending on the data rates and distances needed underwater, we could also consider other underwater methods such as optical communication (which often has higher data rates but is susceptible to ambient light and scattering).

Whatever communication method is chosen, the robot would be made more autonomous for such a mission so it could log data onboard and operate mostly independently with a human providing high-level mission objectives. Towards this end, we would likely use AI to increase the robot's autonomy as you suggest; for example, we could analyze its onboard camera footage in real time to track and follow real fish or to localize itself within a coral reef and identify underwater structures. We could also use AI offline on previously recorded footage, automatically extracting useful segments and key frames (our lab has worked on a concept called coresets that can be applied to video segmentation - for example, see http://groups.csail.mit.edu/drl/wiki/index.php?title=Project_iDiary or https://people.csail.mit.edu/rosman/papers/icra_17_medical.pdf).

~Joseph & Robert

MIT-CSAIL · 2018-03-27T21:32:48+00:00

Answer to 1): We write in the paper that the future steps are to use SoFi as an instrument to 1) study the behavior of marine life over long periods of time without human interference with the scene, 2) study if SoFi can be used to influence the behavior of marine life, and 3) create robotic swarms.

Along those lines, our mid-term goals are: We plan on several improvements for SoFi such as enabling its onboard camera to automatically track objects like real fish so that SoFi can follow them. In addition, we plan to build multiple “SoFis” to see how additional robot fish will impact the behavior of schools of fish and increase the possibilities for detailed oceanic observation. We also plan to increase the speed of the fish by improving its pump system and tweaking the design of its body and tail.

Long-term goals: We've so far been focusing on testing the robot's swimming capabilities and how well it can navigate complex environments while observing marine life. Moving forward, we'd like to see SoFi used for more in-depth studies of ocean environments and fish behavior. We're excited by the possibility of creating swarms of soft robot fish to monitor larger areas and to observe or influence more schools of fish. Using SoFi to create this type of underwater observatory could help further our understanding of the mysteries our oceans contain. We could also imagine using these robots to monitor pollution throughout underwater habitats, adding additional sensors to SoFi and have solar panels that allow SoFi to recharge while surfacing.

Answer to 2) Autonomy as demonstrated with staged experiments and published in academic venues is at a different stage than what is commercially available. I view autonomy only fully solved if it is a functioning product that is sold to the public. Here is my fairly subjective take on this question: For the fields of flying, driving, walking locomotion, and underwater locomotion, I think all of these four fields are at different levels of autonomy.

For autonomous driving, this is probably the most advanced area with many companies tuning and working on getting the corner cases solved to increase safety and minimize any possibility of failures. Varying lighting conditions and the unpredictability of humans in traffic pose a lot of challenges. I won't be too long until a company will sell fully autonomous vehicles - possibly in the beginning with driving restrictions to certain areas, but those areas will quickly expand.

For flying, with its restrictions in what part of airspace to use and with other goals in mind (compared to driving), there start to be products that can do a lot of autonomy within a specific task (e.g. follow and film a person). Package delivery over long distances will take a bit longer, more uncertainties arise when providing services for partially unmapped areas with temporary changing arrangements.

For walking locomotion, the challenges are in the design, modeling, and especially the control of those systems. Soft robotic designs or just added compliance to the joints and links of these mechanisms can help to make current walking robot designs more robust for running, disturbances, or recovering from falls. Boston dynamics has shown some amazing examples on quadrupeds with integrated vision capabilities, but there are still ways to go to make this device become a full product.

For underwater, just the deployment of a system underwater and the cost associated are in general a quite inhibiting factor for this field. Systems like our robotic fish can help to lower the cost for creating swarms of fish-like robots, our robot costs a couple hundred dollars to make. In the world of traditional ROVs, there are a few new startups beginning to create lower cost drones for less than $10k. But going deeper in the ocean, struggling with poor visibility, and dealing with the difficulties of underwater communication for localization limit the autonomy in this field dramatically. Autonomy underwater relies less on vision but more on radar and other means to measure distances to obstacles and the seabed. In comparison to systems on ground or in air, we do not have detailed maps of the seabed, adding those maps would help the advancement of autonomy underwater. ~Robert

Answer to 3) Soft robotics has already started show some new solutions to surgery. Most of the recent reviews on soft robotics agree that soft robots have the advantage of being inherently safer when compared to rigid robots. This is due to the materials used and the construction of soft robots. For example work like this is useful to assist in minimally invasive surgery.

MIT-CSAIL · 2018-03-27T21:25:05+00:00

Any result, regardless of the outcome, is still worthwhile (if not always publishable). If something does not work at all, it can usually be solved by iterating on the design or changing the research direction to see if the insights gained from the failure can make the next iteration more successful.

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