Elementa
The figure library Every diagram on this site, at its own address. All are hand-written inline SVG using the site palette, so they stay legible in both themes and paste into a slide deck without becoming a screenshot of a screenshot.
microaneurysms haemorrhages hard exudates cotton wool spots vessels
FigPipelineHierarchiRetina
The HierarchiRetina three-stage pipeline A fundus image enters Stage I screening for referable diabetic retinopathy at grade 2 or above. It then branches to five parallel lesion segmentation models — microaneurysms, haemorrhages, hard exudates, cotton wool spots and vessels — whose masks rejoin the raw image as eight channels into Stage III, the lesion-guided grader. Stage III emits either an ordinal severity grade from one to four, or an ungradable verdict routed to human review. Fundus image Stage I · screening (referable DR, grade ≥ 2) MA HSMoE HE MultiScale EX BrightSpot CWS warm-up Vessels FOV mask Stage III · LG-DRG (8 channels: RGB + 5 masks) Severity grade (CORN ordinal, 1–4) Ungradable (routed to human review)
Fig. 1 — The HierarchiRetina three-stage pipeline: screening, five parallel lesion segmenters, and lesion-guided grading over eight channels.One string under three tokenisations The string "unhappiness" segmented three ways: as subwords un / happi / ness, as a single word token, and as ten individual characters. Each segmentation fixes which distinctions the model is able to represent at its input. unhappiness subword un happi ness word unhappiness char un h a p p i n e s s
Fig. 2 — One string under three tokenisations. The segmentation chosen at the input fixes which distinctions the model is able to represent at all.Attention as a weighted average over values A single query is compared against four keys. The resulting softmax weights — 0.06, 0.61, 0.09 and 0.24 — are shown as horizontal bars, and the output is the sum of the value vectors scaled by those weights. The weights come from content, not from position. query q softmax(q · kᵢ / √d) k₁ 0.06 k₂ 0.61 k₃ 0.09 k₄ 0.24 Σ wᵢ vᵢ weights are content-addressed — nothing here depends on i
Fig. 3 — Attention as a weighted average over values, with weights computed by comparing a query against every key. Nothing in the computation depends on position.Fig. 4 — Permutation equivariance and its repair. Without positional encoding a reordered input yields a merely reordered output; with it, order becomes information.F