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# Report notes (seed 16)

Write your narrative here. `make_report.py` drops each `## section` into the
matching slot of report.pdf; any section you leave empty shows up as a yellow
TODO box in the PDF, so nothing can be silently forgotten.

Keep every claim consistent with the generated tables — you must be able to
defend each one in the viva.

## roll number

2026XXXX16

## a1 discussion

<!-- Acquisition marks, borders, cropping differences, class imbalance.
     Point at specific image IDs from A5_seed_selected_examples_seed16.png.
     Note the 299x299 images vs 256x256 masks and how you handled it. -->
## a2 ablation

<!-- Which single preprocessing change you ran and why it is clinically
     meaningful; the effect on each model (cite the comparison table). -->
## a2 errors

<!-- Eight errors per model. For each: image ID, true label, predicted label,
     confidence, and the explanation. The `evidence` column of
     Results/metrics/errors_*.csv gives you the supporting numbers. -->
## a2 conclusion

<!-- Which model won, and did chest-X-ray pretraining help? -->

## a3 discussion

<!-- Where segmentation fails; whether Dice differs across the four classes
     (cite the ANOVA); what the boundary metrics add over Dice. -->
## a4 discussion

<!-- Does attention behaviour match the classification metrics? Shortcut
     learning? Difference between correct and incorrect predictions? -->
## b2 discussion

<!-- How the EDA drove input size, augmentation and evaluation choices. -->

## b3 ablation

<!-- The imbalance targeted; effect of focal loss on recall, precision, FN, FP,
     mAP and small-polyp recall. -->
## b4 failures

<!-- Dominant failure patterns, each tied to an exploratory finding. -->

## analysis tasks

<!-- Which task was most preprocessing-sensitive; which metric most informative. -->

## analysis models

<!-- Metrics + Grad-CAM overlap + ablation + errors -> one verdict. -->

## limitations

<!-- Dataset quality, annotation quality, split design, generalisation. -->

	# Report notes (seed 16)

	Write your narrative here. `make_report.py` drops each `## section` into the
	matching slot of report.pdf; any section you leave empty shows up as a yellow
	TODO box in the PDF, so nothing can be silently forgotten.

	Keep every claim consistent with the generated tables — you must be able to
	defend each one in the viva.

	## roll number

	2026XXXX16

	## a1 discussion

	<!-- Acquisition marks, borders, cropping differences, class imbalance.
	Point at specific image IDs from A5_seed_selected_examples_seed16.png.
	Note the 299x299 images vs 256x256 masks and how you handled it. -->

	## a2 ablation

	<!-- Which single preprocessing change you ran and why it is clinically
	meaningful; the effect on each model (cite the comparison table). -->

	## a2 errors

	<!-- Eight errors per model. For each: image ID, true label, predicted label,
	confidence, and the explanation. The `evidence` column of
	Results/metrics/errors_*.csv gives you the supporting numbers. -->

	## a2 conclusion

	<!-- Which model won, and did chest-X-ray pretraining help? -->

	## a3 discussion

	<!-- Where segmentation fails; whether Dice differs across the four classes
	(cite the ANOVA); what the boundary metrics add over Dice. -->

	## a4 discussion

	<!-- Does attention behaviour match the classification metrics? Shortcut
	learning? Difference between correct and incorrect predictions? -->

	## b2 discussion

	<!-- How the EDA drove input size, augmentation and evaluation choices. -->

	## b3 ablation

	<!-- The imbalance targeted; effect of focal loss on recall, precision, FN, FP,
	mAP and small-polyp recall. -->

	## b4 failures

	<!-- Dominant failure patterns, each tied to an exploratory finding. -->

	## analysis tasks

	<!-- Which task was most preprocessing-sensitive; which metric most informative. -->

	## analysis models

	<!-- Metrics + Grad-CAM overlap + ablation + errors -> one verdict. -->

	## limitations

	<!-- Dataset quality, annotation quality, split design, generalisation. -->