Hey PaperLedge learning crew, Ernis here, ready to dive into some cutting-edge dental tech! Today, we're unpacking a fascinating paper about using AI to revolutionize how orthodontists plan your braces or aligners.
Think about it: when you go to the orthodontist, they take impressions or, increasingly, use these cool intraoral scanners that create a 3D model of your teeth. But then, the orthodontist has to manually mark specific points on that 3D model – like the tips of your cusps (those pointy things on your teeth), the widest part of each tooth, and where the tooth meets the gumline. These points are like the _GPS coordinates_ for creating a perfect treatment plan.
This paper tackles the challenge of automating that process. Imagine training a computer to identify these landmarks automatically. It's trickier than it sounds!
Limited Data: It's not like there are millions of 3D tooth scans readily available.
Anatomical Variety: Everyone's mouth is different! Teeth vary in size, shape, and position.
Geometric Complexity: We're dealing with 3D shapes, not just flat images, which adds another layer of complexity.
So, how did these researchers tackle this problem? They entered a competition called the 3DTeethLand Grand Challenge at MICCAI 2024 – basically, a showdown for the best AI system for identifying tooth landmarks. Their approach leverages something called a "Point Transformer" – think of it as a super-smart AI that's really good at understanding 3D shapes. They customized this AI to focus on the unique geometry and anatomy of teeth.
The AI works in stages. First, it analyzes the 3D scan to find interesting features, much like a detective looks for clues. Then, it predicts how far each point on the tooth is from the key landmarks. Finally, it uses a clever trick called "graph-based non-minima suppression" to pinpoint the exact locations of those landmarks. It's like finding the highest peak in a mountain range.
The researchers are reporting some really promising results! And, perhaps even more exciting, they're starting to understand why the AI is making the decisions it's making. That's crucial for building trust in these systems and ensuring they're accurate and reliable.
So, why should you care about this research?
For patients: This could lead to faster, more accurate, and potentially more affordable orthodontic treatment. Less time in the chair, more precise aligners – everyone wins!
For orthodontists: This technology could free up their time to focus on the more complex aspects of treatment planning and patient care.
For AI enthusiasts: This is a great example of how AI can be applied to solve real-world problems in healthcare.
"This research has the potential to streamline orthodontic workflows, reduce human error, and ultimately improve patient outcomes."
Here are a couple of questions that popped into my head while reading this:
If AI can identify these landmarks so accurately, could it eventually help us predict how teeth will move during treatment, allowing for even more personalized and effective plans?
How do we ensure that these AI systems are fair and unbiased, considering the anatomical diversity of different populations?
That’s all for today’s deep dive! I hope you found this summary enlightening. Until next time, keep learning!Credit to Paper authors: Tibor Kubík, Oldřich Kodym, Petr Šilling, Kateřina Trávníčková, Tomáš Mojžiš, Jan Matula

Hey PaperLedge learning crew, Ernis here, ready to dive into another fascinating piece of research! Today, we're talking about something called "surface normal estimation," which, trust me, is way cooler than it sounds.
Think of it like this: imagine you're drawing a 3D object, like an apple. To make it look realistic, you need to shade it correctly. Surface normals are basically the directions those shades point – they tell the computer which way each tiny piece of the apple's surface is facing. Knowing this is super important for all sorts of things, from robots understanding the world around them to creating realistic special effects in movies.
Now, researchers have gotten pretty good at figuring out these surface normals from still images. But what about videos? That's where things get tricky. Imagine that apple wobbling. You want the computer to understand the shading consistently as it moves, right? You don't want it flickering and looking weird. That's temporal coherence, and it's been a tough nut to crack.
This paper introduces a new approach called NormalCrafter. Instead of just tacking on some extra bits to existing methods, they're using the power of video diffusion models. Think of these models as super-smart AI that have "seen" tons of videos and learned how objects move and change over time. NormalCrafter leverages this knowledge to make sure the surface normal estimations are smooth and consistent across the entire video.
But here's the clever part: to make sure NormalCrafter really understands what it's looking at, the researchers developed something called Semantic Feature Regularization (SFR). Imagine you're learning a new language. You could just memorize words, or you could try to understand the meaning behind them. SFR does something similar – it helps NormalCrafter focus on the intrinsic semantics of the scene. This makes it more accurate and robust.
To help explain SFR, think of it as giving NormalCrafter a cheat sheet that highlights the important parts of the scene. It tells the AI, "Hey, pay attention to the edges of the apple," or "The light is reflecting off this area." This ensures the AI focuses on the critical details that define the object's shape and how it interacts with light.
They also use a two-stage training process. Imagine learning to draw: first, you sketch the basic shapes (that's the "latent space"), and then you add the fine details and shading (that's the "pixel space"). This two-stage approach helps NormalCrafter preserve spatial accuracy (making sure the shape is right) while also maintaining that long-term temporal consistency (making sure the shading stays smooth over time).
The results? The researchers show that NormalCrafter is better at generating temporally consistent normal sequences, even with complex details in the videos. This is a big deal because it opens up new possibilities for things like:
Improving video editing and special effects: More realistic 3D models from video footage.
Enhancing robot vision: Robots can better understand and interact with their environment.
Advancing augmented reality: More seamless integration of virtual objects into real-world scenes.
So, why should you care about surface normal estimation? Well, if you're a gamer, this could lead to more realistic graphics. If you're interested in robotics, this is a crucial step towards building truly intelligent machines. And if you just appreciate cool tech, this is a fascinating example of how AI is pushing the boundaries of what's possible.
This is a very cool result showing how diffusion models can be used for more than just generating images. It also shows how we can guide these models to focus on the right things.
Now, a few things that popped into my head while reading this:
How well does NormalCrafter handle completely new types of scenes or objects it hasn't been trained on?
Could this technique be adapted to estimate other properties of surfaces, like roughness or reflectivity?
And, could we use this for real-time applications?
Alright learning crew, that's all for this episode of PaperLedge. I hope you found this deep dive into NormalCrafter as interesting as I did. Until next time, keep learning and stay curious!Credit to Paper authors: Yanrui Bin, Wenbo Hu, Haoyuan Wang, Xinya Chen, Bing Wang

Alright learning crew, Ernis here, ready to dive into some brain-tickling science! Today, we're tackling a paper that's all about predicting how waves move through fluids. Think of it like this: imagine dropping a pebble in a pond – those ripples spreading outwards? That’s wave propagation, and it’s way more complicated than it looks!
The researchers behind this paper have built a super cool system called MI2A (Multistep Integration-Inspired Attention). Sounds fancy, right? But don't worry, we'll break it down. Basically, they've combined a few different AI techniques to make really accurate predictions about wave movement.
First, they use something like a super-smart image compressor. Imagine taking a huge photo and making it a tiny file without losing the important details. That's what this part does – it simplifies the wave data into something smaller and easier to handle, what they call a “reduced latent representation”. Think of it like finding the essence of the wave.
Then, they use something called a recurrent neural network (RNN), kind of like a brain with a memory. It remembers what happened a moment ago to predict what will happen next. They also use "attention," which helps the RNN focus on the most important parts of the wave data at any given time. It's like highlighting the crucial parts of a sentence to understand its meaning.
Now, here’s the really clever bit. They were inspired by old-school math methods – specifically, something called “linear multistep methods”. These methods are known for being really stable and accurate over long periods of time. So, they’ve baked some of that mathematical goodness into their AI to make it even better at predicting waves far into the future.
But here’s the thing: predicting waves is hard! Even with all this fancy AI, you can still run into problems with accuracy over time. The wave's phase (where the peaks and troughs are) and its amplitude (how big the waves are) can start to drift, like a slightly out-of-tune instrument.
“Autoregressive predictions are often prone to accumulating phase and amplitude errors over time.”
To fix this, the researchers came up with a clever trick: they trained their AI to pay special attention to both the phase and the amplitude separately. It’s like training a musician to listen for both the pitch and the volume of the notes, rather than just the overall sound. This helps the AI stay much more accurate over longer periods.
To test their MI2A system, they threw it at three different wave problems, each one more complicated than the last:
A simple wave moving in one direction.
A more complex wave described by the "Burgers equation" (don't worry about the name!).
And finally, a two-dimensional shallow water system – think of water sloshing around in a bathtub!
And guess what? MI2A aced the tests! It was much better at predicting the waves accurately over long periods of time compared to other AI models. It was better at keeping track of both the amplitude and the phase, meaning the predictions were much more reliable.
So, why does all this matter? Well, predicting wave behavior is crucial in all sorts of fields:
For engineers: Designing safer bridges and coastal defenses that can withstand strong waves.
For meteorologists: Predicting tsunamis and storm surges to save lives.
For climate scientists: Understanding how ocean currents and waves affect global climate patterns.
This MI2A system is a big step forward in making these predictions more accurate and reliable. It's a promising tool for real-time wave modeling, which means we could get better warnings about dangerous waves and be better prepared for the future!
Now, a couple of things that really got me thinking:
Could this MI2A approach be applied to other areas where we need to predict complex systems, like the stock market or even the spread of diseases?
And how much computing power does a system like this require? Is it something that can be run on a laptop, or does it need a supercomputer? Because that affects how widely it can be used.
Food for thought, learning crew! Until next time, keep those curiosity engines firing!Credit to Paper authors: Indu Kant Deo, Rajeev K. Jaiman

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool tech! Today we're tackling a paper that's all about making self-driving cars see the world more completely, and do it much faster. Intrigued? Let's get into it!
So, imagine you're driving. You're not just seeing the road in front of you; your brain is filling in the gaps – knowing there's probably a whole house behind that fence, even though you only see the top of the roof. Self-driving cars need to do this too, and they use something called LiDAR.
LiDAR is like radar, but with lasers. It bounces laser beams off objects to create a 3D map of the surroundings. But sometimes, the LiDAR data is incomplete – maybe it’s raining, or something’s blocking the signal. That's where "scene completion" comes in. It's like Photoshop for 3D, filling in the missing pieces to give the car a full picture.
Now, the clever folks behind this paper are using something called "diffusion models" for scene completion. Think of it like this: imagine you start with a blurry, noisy image. A diffusion model gradually "cleans" it up, step-by-step, until you have a clear, complete picture. This is amazing for filling in those missing LiDAR data points!
The problem? Diffusion models are SLOW. Like, watching-paint-dry slow. It takes a lot of computational power to go through all those cleaning steps. And in a self-driving car, every millisecond counts!
Okay, so how do we speed things up? That's where this paper's magic comes in. They've developed a new technique called "Distillation-DPO." Let's break that down:
"Distillation": This is like having a super-smart teacher (the original, slow diffusion model) train a faster student (a simpler model). The student learns to mimic the teacher’s results, but much more quickly.
"DPO" (Direct Policy Optimization): This is the really cool part. It's all about preference learning. Instead of just telling the student model what the right answer is, we show it pairs of potential answers and tell it which one is better. It’s like saying, "This completed scene looks more realistic than that one."
The researchers used LiDAR scene evaluation metrics (basically, ways to measure how good a scene completion is) to create these "better vs. worse" pairs. Because these metrics are usually complex and hard to use directly, they leverage them to create the preference data.
So, Distillation-DPO is basically a fast-learning student model that's been trained using preference data, guided by a slower but wiser teacher. This results in much faster and higher quality scene completion!
The results? The researchers claim their method is five times faster than other state-of-the-art diffusion models, while also producing better results. That’s a huge win for self-driving car technology!
"Our method is the first to explore adopting preference learning in distillation to the best of our knowledge and provide insights into preference-aligned distillation."
Why does this matter?
For self-driving car developers: This is a game-changer. Faster, more accurate scene completion means safer and more reliable autonomous vehicles.
For AI researchers: This paper offers a new approach to training diffusion models, potentially applicable to other areas beyond LiDAR scene completion.
For everyone: Ultimately, safer self-driving cars could lead to fewer accidents and more efficient transportation systems.
Here are a couple of thought-provoking questions this paper brings up for me:
Could this "preference learning" approach be used to train AI in other areas where it's hard to define a single "correct" answer, like artistic style transfer or creative writing?
How can we ensure that the LiDAR scene evaluation metrics used to create the preference data are fair and unbiased, so that the AI doesn't learn to perpetuate existing biases in the environment?
This research really highlights the power of combining different AI techniques to solve complex problems. It's exciting to see how these advancements are shaping the future of self-driving technology! And remember, you can check out the code yourself on GitHub: https://github.com/happyw1nd/DistillationDPO.
That’s all for this episode, PaperLedge crew! Keep learning, keep questioning, and I'll catch you next time!Credit to Paper authors: An Zhaol, Shengyuan Zhang, Ling Yang, Zejian Li, Jiale Wu, Haoran Xu, AnYang Wei, Perry Pengyun GU Lingyun Sun

Hey PaperLedge crew, Ernis here! Get ready to dive into some fascinating image generation tech. Today, we're unpacking a paper about a new system called SimpleAR. Now, before your eyes glaze over at the word "autoregressive," let me break it down. Think of it like this: SimpleAR is like an artist who paints a picture pixel by pixel, using what's already been drawn to decide what comes next. It's building the image sequentially, step-by-step.
What's super cool about SimpleAR is that it achieves impressive results without needing a super complicated design. The researchers focused on clever ways to train it and speed up the image creation process. They found that, even with a relatively small model (only 0.5 billion parameters – which, okay, sounds like a lot, but in the world of AI, it's actually quite modest!), SimpleAR can generate high-quality, realistic images at a resolution of 1024x1024 pixels. That's like producing a detailed photo you could print and hang on your wall!
To put it in perspective, they tested SimpleAR on some tough text-to-image challenges. These benchmarks essentially grade how well the AI can create an image that matches a given description. SimpleAR scored really well, showing it's competitive with other, more complex systems.
The team also discovered some interesting tricks to make SimpleAR even better. For example, they used something called "Supervised Fine-Tuning" (SFT). Imagine teaching the AI by showing it a bunch of perfect examples and saying, "Hey, this is what a good image looks like!" They also used "Group Relative Policy Optimization" (GRPO), which is a bit more complex, but think of it as having a group of art critics giving the AI feedback on its style and composition to improve the overall aesthetic and how well it follows the text prompt.
"both supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) training could lead to significant improvements on generation aesthectics and prompt alignment"
SFT: learning from perfect examples.
GRPO: refining style and composition with feedback.
But here's where it gets really interesting. Generating these high-resolution images can take a while. The researchers used clever acceleration techniques, specifically something called "vLLM," to drastically cut down the creation time. The result? SimpleAR can generate a 1024x1024 image in about 14 seconds! That’s a HUGE improvement and makes the technology much more practical.
Think of it like this: imagine you're ordering a custom portrait. Previously, it might have taken days for the artist to complete it. Now, thanks to SimpleAR and these speed optimizations, you can get a near-instant digital version!
So, why does this matter to us, the PaperLedge crew? Well:
For creatives: This opens up new possibilities for generating art, illustrations, and visual content quickly and efficiently. Imagine brainstorming ideas and instantly seeing them visualized.
For developers: SimpleAR's relatively simple architecture and the open-source code provide a great starting point for building custom image generation tools and applications.
For everyone: It shows that we don't always need massive, complex models to achieve impressive AI results. Simplicity and clever optimization can go a long way.
The researchers are sharing their code and findings to encourage more people to explore autoregressive visual generation. They believe it has a lot of untapped potential. You can find the code at https://github.com/wdrink/SimpleAR.
So, as we wrap up, a few thought-provoking questions come to mind:
Could this simpler approach to image generation democratize AI art, making it accessible to more people with limited computing resources?
What are the ethical implications of faster, more efficient image generation? How can we prevent misuse?
Where do you see this tech going next? Could we see SimpleAR-powered tools integrated into everyday applications like photo editing or even video game development?
That's it for this dive into SimpleAR! Let me know your thoughts, crew. Until next time, keep learning and stay curious!Credit to Paper authors: Junke Wang, Zhi Tian, Xun Wang, Xinyu Zhang, Weilin Huang, Zuxuan Wu, Yu-Gang Jiang

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're talking proteins – those tiny workhorses of our cells that do everything from building tissues to fighting off infections. Think of them like LEGO structures, but instead of plastic bricks, they're made of amino acids folded into intricate 3D shapes. These shapes are crucial because they determine what the protein can do.
Now, scientists are using AI, specifically something called multimodal protein language models, to understand and even design new proteins. Imagine teaching a computer to "speak protein"! These models learn from both the protein's amino acid sequence (like the LEGO instruction manual) and its 3D structure (the assembled LEGO model).
But there's a catch! Current models often simplify the 3D structure by breaking it down into "tokens," like labeling each LEGO brick with a color. This loses a lot of the subtle details and relationships between parts. It's like trying to understand a complex sculpture by only looking at a simplified, blocky version. That's the core problem this research tackles.
This paper asks: How can we build better AI models that capture the full complexity of protein structures, not just a simplified version?
The researchers identified two main roadblocks:
Tokenization Loss: Simplifying the 3D structure into tokens throws away valuable information. Think of it like summarizing a novel into bullet points – you lose the nuance and artistry.
Inaccurate Structure Predictions: The AI sometimes struggles to predict the correct 3D structure from the simplified tokens. It's like trying to rebuild the LEGO model from a faulty set of instructions.
To overcome these challenges, they explored a design space of improvements, focusing on:
Better Generative Modeling: Improving how the AI creates new protein structures.
Structure-Aware Architectures: Designing AI models that are better at understanding 3D shapes.
Representation Learning: Teaching the AI to represent protein structures in a more detailed way.
Data Exploration: Feeding the AI better and more diverse examples of protein structures.
The exciting part is, their improvements really paid off! They developed methods that allow the AI to be supervised with more detailed structure information. Their new models were able to generate more diverse protein structures and, crucially, were much better at predicting how proteins would fold. In fact, their 650-million-parameter model actually outperformed larger, 3-billion-parameter models and even rivaled specialized protein folding programs! That's like a smaller, smarter LEGO builder beating a larger, less skilled one.
The effective design methods dramatically improve the structure generation diversity, and notably, folding abilities of our 650M model... even outperforming 3B baselines and on par with the specialized folding models.
This research is a big deal because it opens the door to designing proteins with specific functions, like creating new drugs, developing more efficient enzymes, or even engineering materials with unique properties. Imagine designing proteins that can break down plastic pollution or create sustainable biofuels!
So, why should you care? Well:
For Scientists: This paper provides a roadmap for building better protein language models, which can accelerate research in various fields.
For Biotech Enthusiasts: It highlights the potential of AI to revolutionize drug discovery and protein engineering.
For the Curious: It offers a glimpse into the cutting-edge research that's shaping the future of biotechnology.
This paper got me thinking about a few things.
First, how far away are we from being able to design a protein with any desired function, essentially creating bespoke biomolecules?
Second, if these models are trained on existing protein structures, are we potentially limiting ourselves to only what nature has already "discovered," or can AI truly innovate and create entirely new protein architectures?
And third, could this technology be misused? How do we ensure that protein design is used for good and not for creating harmful biological agents?
Lots to ponder, learning crew. Until next time, keep those intellectual gears turning!Credit to Paper authors: Cheng-Yen, Hsieh, Xinyou Wang, Daiheng Zhang, Dongyu Xue, Fei Ye, Shujian Huang, Zaixiang Zheng, Quanquan Gu

Alright learning crew, Ernis here, ready to dive into some fascinating AI research! Today, we’re tackling a paper about teaching computers to do something many of us still struggle with: complex math!
Now, we all know AI is getting smarter, but can it actually reason its way through tricky problems, especially in math? That’s the big question this paper addresses. The researchers realized that current AI models are held back by a major problem: a lack of really good, challenging math problems to learn from.
Think of it like this: if you want to become a master chef, you can’t just practice making toast. You need to tackle soufflés and complex sauces! It's the same for AI. They need hard problems to truly learn how to reason mathematically.
So, what did these clever researchers do? They created a brand-new dataset called DeepMath-103K. As the name suggests, it contains around 103,000 mathematical problems, carefully designed to be super challenging. We're talking levels 5 to 9 difficulty - think advanced algebra, calculus, and beyond! The really cool part is that each problem has a verifiable answer, meaning the AI can be easily checked to see if it got it right.
They went through a serious process to make sure these problems were unique and genuinely difficult. They even made sure the problems weren't already floating around in other AI training datasets, which could give the AI an unfair advantage. It's like making sure a student doesn't peek at the answer key!
"DeepMath-103K...significantly exceeding existing open resources in challenge."
This dataset isn’t just a collection of problems; it’s a meticulously crafted resource. Each problem comes with not one, but three different solutions generated by another AI! This gives researchers lots of options for how to train their models. It's like having multiple teaching assistants, each offering a slightly different approach to solving the same problem.
And why does this matter? Well, imagine AI being able to solve complex mathematical problems in fields like:
Science: Helping researchers model climate change or discover new drugs
Engineering: Designing safer bridges or more efficient engines
Finance: Developing better risk management strategies
The possibilities are huge!
The researchers trained AI models on DeepMath-103K and showed that they performed significantly better on challenging math benchmarks. This proves that their dataset is effective and can help us build more capable AI reasoning systems.
Best of all, they've made DeepMath-103K publicly available! That means anyone can use it to train their own AI models and contribute to the progress of AI reasoning.
You can find the dataset here: https://github.com/zwhe99/DeepMath
So, some things that popped into my head while reading this paper:
Could this type of dataset be created for other complex reasoning tasks, like legal reasoning or medical diagnosis?
How do we ensure that AI models trained on datasets like DeepMath-103K don't simply memorize solutions but truly learn to reason mathematically?
As AI becomes more capable of solving complex problems, what are the ethical implications of relying on these systems in critical decision-making processes?
That's all for today, learning crew! I hope you found this dive into DeepMath-103K as fascinating as I did. Keep learning, keep questioning, and I'll catch you next time!Credit to Paper authors: Zhiwei He, Tian Liang, Jiahao Xu, Qiuzhi Liu, Xingyu Chen, Yue Wang, Linfeng Song, Dian Yu, Zhenwen Liang, Wenxuan Wang, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu

Hey learning crew, Ernis here, ready to dive into another fascinating piece of research! Today, we're tackling a topic that affects millions: wounds. Not just any scrapes and bruises, but those stubborn, difficult-to-heal wounds that can really impact someone's quality of life.
Now, imagine you're a wound specialist. You're faced with all sorts of wounds – diabetic ulcers, pressure sores, surgical wounds, venous ulcers – each requiring a different approach. Traditionally, figuring out what kind of wound you're dealing with has been a time-consuming and expensive process. But what if we could use AI to speed things up and improve accuracy?
That's exactly what this paper explores! Researchers have developed a deep learning model, think of it as a super-smart computer program, to classify wounds based on images and their location on the body.
So, how does this AI wizardry work? Well, it's a bit like teaching a computer to see and understand the world like a doctor. Here's the breakdown:
The Vision Transformer: This is the computer's "eyes." It analyzes the wound image, picking out important features like shape, color, and texture. It's like showing the computer a photo and it learns to identify the different parts.
Discrete Wavelet Transform (DWT): Think of this as adding a layer of detail. It helps the computer to focus on the low and high-frequency components of the image which helps to identify subtle differences in wound characteristics.
The Location Matters: Where the wound is located on the body also tells a story. A pressure sore on the heel is different than a surgical wound on the abdomen. To capture this, the researchers use a "body map" to tell the computer exactly where the wound is.
Swarm Intelligence: This is where things get really interesting. To fine-tune the AI, the researchers used algorithms inspired by how animal swarms – like gorillas or wolves – optimize their hunting strategies. These algorithms helped the AI to learn the best way to analyze the images and location data.
Think of it like this: you're training a team of AI detectives, each with their own special skills, to solve the mystery of the wound!
So, what were the results? The model, when combined with these animal-inspired optimization techniques, achieved an accuracy of up to 83.42% in classifying wound types. That's pretty impressive! Even using just the image data, the model achieved an accuracy of around 81%.
Why does this matter?
For patients: Faster and more accurate diagnosis means quicker access to the right treatment, potentially leading to faster healing and improved quality of life.
For doctors: This AI tool could assist wound specialists, helping them make more informed decisions and freeing up their time to focus on patient care.
For healthcare systems: Efficient wound classification can reduce healthcare costs by optimizing treatment plans and preventing complications.
This research shows the exciting potential of AI in healthcare. By combining image analysis, location data, and clever optimization techniques, we can create tools that improve the lives of patients and support the work of healthcare professionals. It’s like giving doctors a super-powered diagnostic assistant!
But, it also raises some interesting questions:
Could this technology eventually be used to develop a smartphone app that allows patients to monitor their own wounds and receive personalized care recommendations?
How do we ensure that these AI models are trained on diverse datasets to avoid bias and ensure equitable access to care for all patients?
What do you think, learning crew? Where do you see this technology heading in the future? Let me know your thoughts in the comments!Credit to Paper authors: Ramin Mousa, Hadis Taherinia, Khabiba Abdiyeva, Amir Ali Bengari, Mohammadmahdi Vahediahmar