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u/Aggravating_Dot5315

r/remotejobsfinders r/computervision r/MLQuestions r/developersPak r/deeplearning r/learnprogramming r/learnmachinelearning r/cscareers r/cyprus r/cscareerquestionsPAK
▲ 113 r/learnprogramming

I recently graduated in Computer Science, but AI has killed my passion for programming. How do I find my direction again?

I graduated with a degree in Computer Science about two months ago. For most of my studies, I was genuinely passionate about CS and programming. I enjoyed sitting with a difficult problem, understanding what was going wrong, debugging it, and eventually figuring it out myself. There was a real sense of satisfaction when something finally worked.

Over the last couple of years, though, AI tools have slowly changed the way I feel about programming.

Instead of spending hours investigating a problem myself, I can give it to an AI and often get an answer almost immediately. Even when the AI doesn’t solve it directly, I sometimes feel like I’m fighting with prompts and generated code rather than actually programming. I know these tools can make us much more productive, but personally, they have taken away some of the curiosity and reward I used to get from the process.

At the same time, I’m currently being paid to work on medical/AI research projects with one of my professors. The problem is that our data is extremely limited, and I have serious doubts that the project can produce a meaningful solution with what we currently have. This has put me in a strange situation where I’m getting paid, but I don’t feel productive or useful. Some days there simply isn’t much meaningful work I can do, and that makes me feel even worse.

All of this has made me question my direction.

Before choosing Computer Science, I also considered studying medicine. If I’m being completely truthful with myself, a big part of the attraction was the status, financial security, and clear career path rather than a deep fascination with medicine itself. I ultimately chose CS because I was genuinely more interested in it.

Now that my excitement about programming has decreased, I’ve started wondering whether I made the wrong decision. I catch myself thinking about medicine again and wondering what my life would look like if I had chosen that path.

I’m trying to separate a few things that may be getting mixed together: burnout, disappointment with my current research job, the effect AI has had on programming, comparison with other careers, and the possibility that my interests have genuinely changed.

For people who have gone through something similar:

How did you figure out whether you had actually lost interest in your field or were simply burned out?

How have experienced developers kept programming intellectually rewarding in the age of AI?

And more generally, how do you find your direction again when something you were passionate about suddenly doesn’t give you the same satisfaction anymore?

I’m not necessarily looking for someone to tell me whether to stay in CS or change careers. I’d appreciate perspectives from people who have gone through a similar period and eventually figured out what they actually wanted.

reddit.com
u/Aggravating_Dot5315 — 13 hours ago
▲ 2 r/developersPak

I recently graduated in Computer Science, but AI has killed my passion for programming. How do I find my direction again ?

I graduated with a degree in Computer Science about two months ago. For most of my studies, I was genuinely passionate about CS and programming. I enjoyed sitting with a difficult problem, understanding what was going wrong, debugging it, and eventually figuring it out myself. There was a real sense of satisfaction when something finally worked.

Over the last couple of years, though, AI tools have slowly changed the way I feel about programming.

Instead of spending hours investigating a problem myself, I can give it to an AI and often get an answer almost immediately. Even when the AI doesn’t solve it directly, I sometimes feel like I’m fighting with prompts and generated code rather than actually programming. I know these tools can make us much more productive, but personally, they have taken away some of the curiosity and reward I used to get from the process.

At the same time, I’m currently being paid to work on medical/AI research projects with one of my professors. The problem is that our data is extremely limited, and I have serious doubts that the project can produce a meaningful solution with what we currently have. This has put me in a strange situation where I’m getting paid, but I don’t feel productive or useful. Some days there simply isn’t much meaningful work I can do, and that makes me feel even worse.

All of this has made me question my direction.

Before choosing Computer Science, I also considered studying medicine. If I’m being completely truthful with myself, a big part of the attraction was the status, financial security, and clear career path rather than a deep fascination with medicine itself. I ultimately chose CS because I was genuinely more interested in it.

Now that my excitement about programming has decreased, I’ve started wondering whether I made the wrong decision. I catch myself thinking about medicine again and wondering what my life would look like if I had chosen that path.

I’m trying to separate a few things that may be getting mixed together: burnout, disappointment with my current research job, the effect AI has had on programming, comparison with other careers, and the possibility that my interests have genuinely changed.

For people who have gone through something similar:

How did you figure out whether you had actually lost interest in your field or were simply burned out?

How have experienced developers kept programming intellectually rewarding in the age of AI?

And more generally, how do you find your direction again when something you were passionate about suddenly doesn’t give you the same satisfaction anymore?

I’m not necessarily looking for someone to tell me whether to stay in CS or change careers. I’d appreciate perspectives from people who have gone through a similar period and eventually figured out what they actually wanted.

reddit.com
u/Aggravating_Dot5315 — 13 hours ago
▲ 2 r/computervision

[D] How can I improve cross-patient generalization on a small hysteroscopy dataset with correlated frames?

I am working with the hysteroscopy dataset, which contains:

  • 3,385 frames from 175 patients.
  • Eight lesion classes, labelled from 0 to 7.
  • A highly imbalanced number of patients and frames across classes.
  • Multiple correlated frames from each patient.
  • Some frames containing more than one lesion class.

Before attempting the complete multiclass problem, I reduced it to a binary subset to verify that the training and evaluation pipeline works correctly.

Current binary subset

  • Selected lesion classes: 2 and 3.
  • Total: 1,575 frames from 113 unique patients.
  • Class 2: 1,054 frames from 78 patients.
  • Class 3: 521 frames from 36 patients.
  • One patient has different frames belonging to both classes but remains entirely within one split.

Patient-disjoint split

  • Training: 1,095 frames from 79 patients.
  • Validation: 241 frames from 17 patients.
  • Testing: 239 frames from 17 patients.
  • No patient appears in more than one subset.
  • The frame-level class distribution is approximately 67%/33% in every subset.

Approaches I have tried

  • DenseNet121, ViT, and DINOv2 backbones.
  • Frozen pretrained backbone with only the classifier trained.
  • Different classifier-head sizes and dropout.
  • Class-weighted cross-entropy.
  • Mild and stronger image augmentations.
  • Early stopping and learning-rate scheduling.
  • Unfreezing the final one or two encoder blocks.

With the correct patient-level split, training performance improves, but validation performance generally plateaus or deteriorates, and performance on unseen test patients remains relatively low.

As a diagnostic, I also tried a random frame-level split and obtained substantially better results. However, this evaluation is invalid because correlated frames from the same patients appear across training, validation, and testing, causing patient leakage and inflated performance.

I would appreciate advice on how to improve generalization to unseen patients in this setting.

u/Aggravating_Dot5315 — 27 days ago
▲ 2 r/MLQuestions

[D] How can I improve cross-patient generalization on a small hysteroscopy dataset with correlated frames?

I am working with hysteroscopy dataset, which contains:

  • 3,385 frames from 175 patients.
  • Eight lesion classes, labelled from 0 to 7.
  • A highly imbalanced number of patients and frames across classes.
  • Multiple correlated frames from each patient.
  • Some frames containing more than one lesion class.

Before attempting the complete multiclass problem, I reduced it to a binary subset to verify that the training and evaluation pipeline works correctly.

Current binary subset

  • Selected lesion classes: 2 and 3.
  • Total: 1,575 frames from 113 unique patients.
  • Class 2: 1,054 frames from 78 patients.
  • Class 3: 521 frames from 36 patients.
  • One patient has different frames belonging to both classes but remains entirely within one split.

Patient-disjoint split

  • Training: 1,095 frames from 79 patients.
  • Validation: 241 frames from 17 patients.
  • Testing: 239 frames from 17 patients.
  • No patient appears in more than one subset.
  • The frame-level class distribution is approximately 67%/33% in every subset.

Approaches I have tried

  • DenseNet121, ViT, and DINOv2 backbones.
  • Frozen pretrained backbone with only the classifier trained.
  • Different classifier-head sizes and dropout.
  • Class-weighted cross-entropy.
  • Mild and stronger image augmentations.
  • Early stopping and learning-rate scheduling.
  • Unfreezing the final one or two encoder blocks.

With the correct patient-level split, training performance improves, but validation performance generally plateaus or deteriorates, and performance on unseen test patients remains relatively low.

As a diagnostic, I also tried a random frame-level split and obtained substantially better results. However, this evaluation is invalid because correlated frames from the same patients appear across training, validation, and testing, causing patient leakage and inflated performance.

I would appreciate advice on how to improve generalization to unseen patients in this setting.

u/Aggravating_Dot5315 — 27 days ago
▲ 1 r/deeplearning

[D] How can I improve cross-patient generalization on a small hysteroscopy dataset with correlated frames?

I am working with the HS-CMU hysteroscopy dataset, which contains:

  • 3,385 frames from 175 patients.
  • Eight lesion classes, labelled from 0 to 7.
  • A highly imbalanced number of patients and frames across classes.
  • Multiple correlated frames from each patient.
  • Some frames containing more than one lesion class.

Before attempting the complete multiclass problem, I reduced it to a binary subset to verify that the training and evaluation pipeline works correctly.

Current binary subset

  • Selected lesion classes: 2 and 3.
  • Total: 1,575 frames from 113 unique patients.
  • Class 2: 1,054 frames from 78 patients.
  • Class 3: 521 frames from 36 patients.
  • One patient has different frames belonging to both classes but remains entirely within one split.

Patient-disjoint split

  • Training: 1,095 frames from 79 patients.
  • Validation: 241 frames from 17 patients.
  • Testing: 239 frames from 17 patients.
  • No patient appears in more than one subset.
  • The frame-level class distribution is approximately 67%/33% in every subset.

Approaches I have tried

  • DenseNet121, ViT, and DINOv2 backbones.
  • Frozen pretrained backbone with only the classifier trained.
  • Different classifier-head sizes and dropout.
  • Class-weighted cross-entropy.
  • Mild and stronger image augmentations.
  • Early stopping and learning-rate scheduling.
  • Unfreezing the final one or two encoder blocks.

With the correct patient-level split, training performance improves, but validation performance generally plateaus or deteriorates, and performance on unseen test patients remains relatively low.

As a diagnostic, I also tried a random frame-level split and obtained substantially better results. However, this evaluation is invalid because correlated frames from the same patients appear across training, validation, and testing, causing patient leakage and inflated performance.

I would appreciate advice on how to improve generalization to unseen patients in this setting.

u/Aggravating_Dot5315 — 27 days ago
▲ 2 r/learnmachinelearning

[D] How can I improve cross-patient generalization on a small hysteroscopy dataset with correlated frames?

I am working with the HS-CMU hysteroscopy dataset, which contains:

  • 3,385 frames from 175 patients.
  • Eight lesion classes, labelled from 0 to 7.
  • A highly imbalanced number of patients and frames across classes.
  • Multiple correlated frames from each patient.
  • Some frames containing more than one lesion class.

Before attempting the complete multiclass problem, I reduced it to a binary subset to verify that the training and evaluation pipeline works correctly.

Current binary subset

  • Selected lesion classes: 2 and 3.
  • Total: 1,575 frames from 113 unique patients.
  • Class 2: 1,054 frames from 78 patients.
  • Class 3: 521 frames from 36 patients.
  • One patient has different frames belonging to both classes but remains entirely within one split.

Patient-disjoint split

  • Training: 1,095 frames from 79 patients.
  • Validation: 241 frames from 17 patients.
  • Testing: 239 frames from 17 patients.
  • No patient appears in more than one subset.
  • The frame-level class distribution is approximately 67%/33% in every subset.

Approaches I have tried

  • DenseNet121, ViT, and DINOv2 backbones.
  • Frozen pretrained backbone with only the classifier trained.
  • Different classifier-head sizes and dropout.
  • Class-weighted cross-entropy.
  • Mild and stronger image augmentations.
  • Early stopping and learning-rate scheduling.
  • Unfreezing the final one or two encoder blocks.

With the correct patient-level split, training performance improves, but validation performance generally plateaus or deteriorates, and performance on unseen test patients remains relatively low.

As a diagnostic, I also tried a random frame-level split and obtained substantially better results. However, this evaluation is invalid because correlated frames from the same patients appear across training, validation, and testing, causing patient leakage and inflated performance.

I would appreciate advice on how to improve generalization to unseen patients in this setting.

u/Aggravating_Dot5315 — 27 days ago
▲ 3 r/deeplearning+1 crossposts

Seeking Advice on Hysteroscopy Lesion Classification with Transfer Learning

I'm working on 9-class hysteroscopy lesion classification (lesion classes 0–7 + no_lesion) using the HS-CMU and HS-CMU-V2 datasets with patient-level cascading stratified splits (70/15/15).
Dataset: 5,675 images. Severe class imbalance:
Class 0: 228 images (17 patients)
Class 1: 145 images (11 patients)
Class 2: 1,403 images (152 patients)
Class 3: 379 images (43 patients)
Class 4: 199 images (14 patients)
Class 5: 240 images (24 patients)
Class 6: 396 images (36 patients)
Class 7: 95 images (7 patients)
Class 8 (no_lesion): 806 images (50 patients)

Models tried: DenseNet-121 and DINOv2-small, both pretrained.

My issue is that: Validation Macro-F1 stays around 0.30 across different setups. Training metrics improve but validation doesn't follow. Tried various optimizers, schedulers, and augmentation strategies.

Is this a domain gap issue or data limitation?
Should I switch to medical-pretrained encoders?
Is 1-3 patients per class in val/test too few for reliable metrics?
Any tips or suggestions would be greatly appreciated I want to solve this issue so as to train properly feature extractors!

reddit.com
u/Aggravating_Dot5315 — 1 month ago
▲ 3 r/deeplearning

Problem on how to split a multiclass medical dataset

Hello everyone,

I am working on a problem trying to train a baseline image classification model to use it later as a feature extractor. However, my issue is that my data are very imbalanced, there are total 8 different lesion categories and a huge gap between the number of patients in each class.

How would I ensure a correct way of splitting them into train,val and test sets and guaranteeing that each class appears in each set apart from doing it in a hardcoded way?

Below is the number of data and patients in each class for your reference:

TRAIN no_lesion: 542 0: 205 1: 131 2: 1393 3: 296 4: 216 5: 300 6: 310 7: 94

VAL_SAMPLES no_lesion: 337 0: 86 1: 59 2: 296 3: 137 4: 0 5: 65 6: 173 7: 12

TEST_SAMPLES no_lesion: 305 0: 75 1: 47 2: 374 3: 119 4: 64 5: 85 6: 69 7: 0

class no_lesion: 50 patients carry this class
class 0: 17 patients carry this class
class 1: 12 patients carry this class
class 2: 153 patients carry this class
class 3: 43 patients carry this class
class 4: 14 patients carry this class
class 5: 24 patients carry this class
class 6: 36 patients carry this class
class 7: 7 patients carry this class

reddit.com
u/Aggravating_Dot5315 — 2 months ago
▲ 4 r/remotejobsfinders+1 crossposts

Looking for Flexible Part-Time Work | Tech Background, Open to Other Roles

Hi everyone,

I recently graduated from the UCY with a BSc in Computer Science and I’m currently working as a Research Assistant in biomedical AI, developing deep learning models.

Since my work is mostly remote and flexible, I’m looking for an additional part-time opportunity, ideally in the tech field. I’d be interested in software development, backend development, AI/ML, automation, data-related work, or general IT support.

I’m also open to other flexible part-time jobs, even outside tech, that could help me earn some extra income alongside my current work.

If you know of any openings or have any recommendations, I’d really appreciate it.
Thank you!

reddit.com
u/Aggravating_Dot5315 — 2 months ago
▲ 9 r/cscareerquestionsPAK+2 crossposts

Recent Computer Science Graduate Looking for Advice

Hello guys,

I am graduating in a few days from the University of Cyprus with a degree in Computer Science. I recently received a decent offer to work in research, and apart from that, I was wondering what other opportunities I could pursue to earn some extra income.

I plan to work for a year before continuing with a Master’s degree abroad. During this time, I would like to strengthen my CV as much as possible while also saving some money.

I would appreciate any advice on opportunities that could help with both goals. I am also interested in hearing how others have managed to balance work, career development, and personal time without burning themselves out.

Thank you for taking the time to read this. Any advice would be greatly appreciated.

reddit.com
u/Aggravating_Dot5315 — 3 months ago