Revise README structure and project description

Reorganized and clarified the README content, including the project description and key features.

README.md

@@ -15,14 +15,13 @@
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 # WSAttention-Prostate
-
-**Weakly Supervised Attention-Based Deep Learning for Prostate Cancer Characterization from Bi-Parametric Prostate MRI.**
-
+Predicts PI-RADS score and risk of clinically significant prostate cancer (csPCa) from T2-Weighted (T2W), Diffusion Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) sequences of bi-paramteric MRI (bpMRI).
 
 ## 🚀 Platform Access
 Real-time inference via [GUI](https://huggingface.co/spaces/anirudh0410/Prostate-Inference)
 
-Predicts PI-RADS score and risk of clinically significant prostate cancer (csPCa) from T2-Weighted (T2W), Diffusion Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) sequences of bi-paramteric MRI (bpMRI).
+## Weakly Supervised Attention-Based Deep Learning for Prostate Cancer Characterization from Bi-Parametric Prostate MRI.
+
 
 Deep learning methods used in medical AI—particularly for csPCa prediction and PI-RADS classification—typically rely on expert-annotated labels for training, which limits scalability to larger datasets and broader clinical adoption. To address this, we employ a two-stage multi-instance learning (MIL) framework pretrained using attention-based weak supervision on scan-level PI-RADS scores. For downstream risk assessment, the PI-RADS classification head is replaced and fine-tuned on a substantially smaller dataset to predict clinically significant prostate cancer (csPCa). Careful preprocessing is applied to mitigate variability arising from cross-site MRI acquisition differences. For further details, please refer to our paper or visit the project website.