Research task (AI & ML):
Design a modified U-Net architecture to perform instance segmentation on noisy fluorescence microscopy images with overlapping cells or organelles. Describe the U-Net variant used (e.g., attention, residual, or transformer-based) and justify the architectural choices. Explain the loss functions and postprocessing techniques used to separate touching objects. Include Python code for key components (e.g., model, loss). Compare your approach to 2–3 existing models (e.g., Cellpose, Stardist, Mask R-CNN). 

Evaluation rubric:
criterion: Specifies a chosen U-Net variant (e.g., attention, residual, transformer, UNet++ etc.)
weight: 5
axis: Explicit Criteria

criterion: Explains how the chosen modifications improve handling of noise and overlapping objects in microscopy images.
E.g. -
1. Attention U-Net - Attention gates suppress background fluorescence and highlight bright cell regions, improving segmentation in noisy images and separating overlapping nuclei.”
2. Residual U-Net - Residual blocks stabilize training under noise and preserve weak edges, helping recover boundaries of faint or overlapping organelles.
3. Transformer U-Net - Global attention captures context across the image, allowing the model to distinguish overlapping cells even when local boundaries are unclear.
4. UNet++ - Dense skip connections fuse features across scales, enhancing detection of dim, small structures and improving separation in cluttered microscopy images.
weight: 5
axis: Explicit Criteria

criterion: Describes the loss function(s) used (e.g., Dice, focal loss, boundary-aware loss, composite loss (Dice + binary cross entropy) etc.).
weight: 5
axis: Explicit Criteria

criterion: Includes Python code implementing the modified U-Net architecture.
weight: 5
axis: Explicit Criteria

criterion: Includes Python code for the custom loss function(s).
weight: 5
axis: Explicit Criteria

criterion: Describes the post-processing techniques used to separate touching objects (e.g., thresholding + watershed on boundary maps, or connected-component labeling etc,). The description should go beyond code and provide a clear textual explanation of each step.
weight: 5
axis: Explicit Criteria

criterion: Describes the full pipeline from input image to postprocessed output, covering all major stages. (e.g., preprocessing, model inference, post-processing for instance separation etc.). This could be in the form of code, text, digram or a combination of all of them.
weight: 5
axis: Implicit Criteria

criterion: Structures the response logically with clearly labeled sections for architecture, loss, code, and comparison.
weight: 3
axis: Communication Quality

criterion: Demonstrates understanding of domain-specific challenges (noise, low contrast, overlapping structures, biological variability) in microscopy images.
weight: 3
axis: Implicit Criteria

criterion: Justifies architectural and methodological choices based on these domain challenges.
weight: 3
axis: Synthesis of Information

criterion: Makes unrealistic or irrelevant design assumptions not applicable to fluorescence microscopy. E.g. - 
1. Using ImageNet-pretrained RGB models without adapting them to grayscale or multi-channel fluorescence inputs.
2. Assuming uniform illumination and no noise, even though fluorescence images often have uneven lighting and photon noise.
3. Designing a model for natural scene objects (cars, dogs, etc.) instead of biological structures like cells or organelles.
4. Using postprocessing methods that rely on color cues or texture gradients that don’t exist in fluorescence images.
5. Assuming that object boundaries are always sharp and distinct, ignoring the diffuse or overlapping nature of fluorescence signals.
weight: -4
axis: Implicit Criteria

criterion: Provides code that is syntactically correct and runnable for key components (model, loss). E.g. - the code should not be truncated, correct libraries are not imported, invalid python syntax etc.)
weight: 4
axis: Implicit Criteria

criterion: Critically compares strengths and weaknesses of the proposed approach relative to at least two existing methods.
weight: 4
axis: Explicit Criteria

criterion: Fails to discuss or address core instance segmentation challenges (e.g., noise robustness, instance separation).
weight: -3
axis: Implicit Criteria

criterion: Explains the architecture of the chosen U-Net variant. This could be in the form of code, text, digram or a combination of all of them.
weight: 4
axis: Implicit Criteria

criterion: Provides a clear, detailed justification for the chosen U-Net variant, explaining the purpose and reasoning behind each major component or block of the architecture.”
weight: 3
axis: Communication Quality

criterion: Explains how each loss function contributes to optimizing instance segmentation. For instance, Dice loss ensures accurate region overlap between predicted and true masks, focal loss emphasizes hard-to-segment cells in imbalanced data, and boundary loss sharpens object edges to separate touching instances.
weight: 3
axis: Implicit Criteria

criterion: Lacks a detailed training and data augmentation plan. For example, the response mentions model training but provides no command to run the code (e.g., no python train.py --config configs/fluoro.yaml), no environment/setup steps, hard-codes hyper-parameters in the script, and omits data augmentation steps such as rotation, flipping, or noise injection commonly used in fluorescence microscopy.
weight: -3
axis: Implicit Criteria

criterion: Does not propose an ablation study. For example, the report presents results for the final model only and does not compare performance with or without key components such as attention gates, boundary loss, or postprocessing steps.
weight: -3
axis: Implicit Criteria

criterion: Omits implementation code for the post-processing pipeline.
weight: -3
axis: Implicit Criteria

criterion: Defines target quantitative performance metrics. For Example, dice coefficient, Intersection over Union (IoU), and Boundary F1-score as evaluation targets for segmentation accuracy, and mean Average Precision (mAP) for instance detection.
weight: 3
axis: Implicit Criteria

criterion: Describes data preprocessing steps specific to fluorescence microscopy images, such as background subtraction to remove uneven illumination, flat-field correction, intensity normalization across channels, Gaussian or median filtering to suppress photon noise etc.
weight: 4
axis: Implicit Criteria

criterion: The response specifies computational requirements and hardware constraints for training and inference. For example, the response mentions that the training requires an NVIDIA RTX 3090 (24 GB VRAM) or equivalent, 16 GB RAM, and ~4 hours for 100 epochs on 512×512 images; inference runs at ~5 frames/sec on GPU or 0.5 frames/sec on CPU.
weight: 3
axis: Implicit Criteria