u/Future-Structure-296

Hi everyone,

I’m working on a 2-class classification problem (LCA vs. RCA coronary arteries) using 2D X-ray angiograms. I’m currently stuck in a cycle of extreme overfitting and could use some advice on my training strategy.

The Setup:

• Dataset: Small (~900 training frames from ~300 unique DICOMs).
• Architecture: InceptionV3 (PyTorch).
• Input: Grayscale .npy arrays converted to 3-channel, resized to 299x299.
• Current Strategy: Transfer learning from ImageNet. I’ve tried full unfreezing and partial unfreezing (last blocks).

The Problem: My training accuracy hits ~95-99% within a few epochs, but validation accuracy peaks early (around 74-79%) and then collapses toward 30-40% as the model starts memorizing the specific textures of the training patients.

What I’ve Tried So Far:

1. Normalization: Standard ImageNet mean/std (applied at load time).
2. Class Weights: Handled 2:1 imbalance (LCA:RCA).
3. Regularization: Added Dropout (tried 0.3 to 0.6) and Weight Decay (1e-4).
4. Augmentation: Flips, 25deg rotations, and translation.
5. Schedulers: ReduceLROnPlateau (factor 0.5, patience 8).

Would love any insights or papers you'd recommend for small-sample medical classification. Thanks!

Hi everyone,

I’m working on a 2-class classification problem (LCA vs. RCA coronary arteries) using 2D X-ray angiograms. I’m currently stuck in a cycle of extreme overfitting and could use some advice on my training strategy.

The Setup:

• Dataset: Small (~900 training frames from ~300 unique DICOMs).
• Architecture: InceptionV3 (PyTorch).
• Input: Grayscale .npy arrays converted to 3-channel, resized to 299x299.
• Current Strategy: Transfer learning from ImageNet. I’ve tried full unfreezing and partial unfreezing (last blocks).

The Problem: My training accuracy hits ~95-99% within a few epochs, but validation accuracy peaks early (around 74-79%) and then collapses toward 30-40% as the model starts memorizing the specific textures of the training patients.

What I’ve Tried So Far:

1. Normalization: Standard ImageNet mean/std (applied at load time).
2. Class Weights: Handled 2:1 imbalance (LCA:RCA).
3. Regularization: Added Dropout (tried 0.3 to 0.6) and Weight Decay (1e-4).
4. Augmentation: Flips, 25deg rotations, and translation.
5. Schedulers: ReduceLROnPlateau (factor 0.5, patience 8).

Would love any insights or papers you'd recommend for small-sample medical classification. Thanks!

Hi everyone,

I’m planning to start my master’s this October and I’m deciding between two options.

Option A: A Master’s in Data Science and Machine Learning.
This matches my current role as a Machine Learning Engineer and feels closely aligned with my background and career path.

Option B: A broader Master’s in Computer Science with a Data Science module.
It is less specialized, but the university has stronger overall prestige, recognition, and research output.

My long-term goal is to keep growing in AI and ML, both as an engineer and potentially in research. I’m also considering teaching in the future. Option A might make that easier because I did my bachelor’s there and already have connections with faculty.

Another thing I’m thinking about is access to research tools, mentorship, and opportunities to work with professors. I feel Option A may be stronger for that, even though Option B has the bigger name.

For people working in ML, data science, research, academia, or hiring: how would you think about this choice?

Hi everyone,

I’m planning to start my master’s this October and I’m deciding between two options.

Option A: A Master’s in Data Science and Machine Learning.
This matches my current role as a Machine Learning Engineer and feels closely aligned with my background and career path.

Option B: A broader Master’s in Computer Science with a Data Science module.
It is less specialized, but the university has stronger overall prestige, recognition, and research output.

My long-term goal is to keep growing in AI and ML, both as an engineer and potentially in research. I’m also considering teaching in the future. Option A might make that easier because I did my bachelor’s there and already have connections with faculty.

Another thing I’m thinking about is access to research tools, mentorship, and opportunities to work with professors. I feel Option A may be stronger for that, even though Option B has the bigger name.

For people working in ML, data science, research, academia, or hiring: how would you think about this choice?

Hi everyone,

I’m planning to start my master’s this October and I’m deciding between two options.

Option A: A Master’s in Data Science and Machine Learning.
This matches my current role as a Machine Learning Engineer and feels closely aligned with my background and career path.

Option B: A broader Master’s in Computer Science with a Data Science module.
It is less specialized, but the university has stronger overall prestige, recognition, and research output.

My long-term goal is to keep growing in AI and ML, both as an engineer and potentially in research. I’m also considering teaching in the future. Option A might make that easier because I did my bachelor’s there and already have connections with faculty.

Another thing I’m thinking about is access to research tools, mentorship, and opportunities to work with professors. I feel Option A may be stronger for that, even though Option B has the bigger name.

For people working in ML, data science, research, academia, or hiring: how would you think about this choice?

Hi everyone,

I’m thinking about slowly buying physical gold, and maybe some silver, as a long-term way to preserve part of my savings. I’m not trying to trade it or flip it for profit. The idea is more to “freeze” some money so it hopefully holds value against inflation over the next 10+ years.

I’d probably buy a small amount every month from my salary. My salary isn’t huge, so I’m trying to figure out whether it makes more sense to buy smaller pieces regularly, or save up for larger bars/coins because the price per gram and spread are usually better. The thing I’m unsure about is that while I’m saving up, gold prices could rise, so waiting also has some risk.

I’ve also noticed that different brands can have very different prices even when the weight and fineness are the same, like 20g and 999.9 gold. Some bars have much better spreads than others. So I’m wondering how much the brand really matters if it’s a known refinery, versus just buying the lowest-spread recognizable bar or coin.

For someone doing this long-term and slowly, would you focus on small monthly purchases, saving up for bigger pieces, or some mix of both? Also, would you stick mostly to gold, or include some silver too?

Any beginner advice or mistakes to avoid would be appreciated or even should I consider this, I know what stocks some people say are better, but from where I come it is not easy to invest in stocks and the broker rates are crazy. Thanks