docs: update the README file

Author: yousefkotp

README.md

@@ -13,7 +13,7 @@
   <img src="https://raw.githubusercontent.com/AtlasAnalyticsLab/MOOZY/main/assets/overview_0.5.png" alt="MOOZY: a patient-first foundation model for computational pathology, whole-slide image encoding, and case-level representation learning">
 </p>
 
-**MOOZY is a foundation model for computational pathology that treats the patient case, not the individual slide, as the fundamental unit of representation.** It encodes one or more whole-slide images (WSIs) into a single 768-dimensional case-level embedding that captures dependencies across all slides from the same patient. Trained entirely on public data with 85.8M parameters (14x smaller than GigaPath), MOOZY outperforms larger models on classification and survival prediction tasks across diverse organs and cancer types.
+**MOOZY is a foundation model for computational pathology that treats the patient case, not the individual slide, as the fundamental unit of representation.** It encodes one or more whole-slide images (WSIs) into a single 768-dimensional case-level embedding that captures dependencies across all slides from the same patient. Trained entirely on public data with 85.8M parameters (14x smaller than GigaPath), MOOZY outperforms larger models on classification tasks across diverse organs and cancer types.
 
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@@ -179,7 +179,7 @@ Frozen-feature MLP probe comparison against slide encoder baselines on eight hel
 | | AUC | **0.70** | 0.68 | 0.69 | 0.59 | 0.60 | 0.68 |
 | | Bal Acc | 0.48 | 0.40 | **0.51** | 0.35 | 0.37 | 0.48 |
 
-<p align="center"><sub>Mean values from five-fold frozen-feature evaluation. Full results with confidence intervals are in the <a href="https://arxiv.org/abs/2603.27048">paper</a>.</sub></p>
+<p align="center"><sub>Mean values from five-fold frozen-feature evaluation. Full results are in the <a href="https://arxiv.org/abs/2603.27048">paper</a>.</sub></p>
 
 Across all eight tasks, MOOZY improves macro averages over TITAN by +7.4% weighted F1, +5.5% AUC, and +7.8% balanced accuracy, and over PRISM by +8.8% F1, +10.7% AUC, and +9.8% balanced accuracy, with 14x fewer parameters than GigaPath.