Update README.md

README.md

@@ -48,13 +48,13 @@ Medical imaging datasets are increasingly available, yet abnormal and annotation
 The overall pipeline for organ, body, and nodule segmentation with alignment is shown below:
 
 <p align="center">
-  <img src="https://github.com/fitushar/NoMAISI/blob/main/doc/images/workflow.png" alt="Segmentation Pipeline"/>
+  <img src="doc/images/workflow.png" alt="Segmentation Pipeline"/>
 </p>
 
 **Workflow** for constructing the **NoMAISI** development dataset. The pipeline includes **(1)** organ segmentation using AI models, **(2)** body segmentation with algorithmic methods, **(3)** nodule segmentation through AI-assisted and ML-based refinement, and **(4)** segmentation alignment to integrate organs, body, and nodules segmentations into anatomically consistent volumes.
 
 <p align="center">
-  <img src="https://github.com/fitushar/NoMAISI/blob/main/doc/images/NoMAISI_train_and_infer.png" alt="NoMAISI_train_and_infer"/>
+  <img src="doc/images/NoMAISI_train_and_infer.png" alt="NoMAISI_train_and_infer"/>
 </p>
 
 **Overview** of our flow-based latent diffusion model with ControlNet conditioning for AI-based CT generation. The pipeline consists of three stages: **(top) Pretrained VAE** for image compression, where CT images are encoded into latent features using a frozen VAE; **(middle)** Model fine-tuning, where a **Rectified Flow ODE sampler**, conditioned on segmentation masks and voxel spacing through a **fine-tuned ControlNet**, predicts velocity fields in latent space and is optimized with a region-specific contrastive loss emphasizing ROI sensitivity and background consistency; and **(bottom) Inference**, where segmentation masks and voxel spacing guide latent sampling along the ODE trajectory to obtain a clean latent representation, which is then decoded by the VAE into full-resolution AI-generated CT images conditioned by body and lesion masks.
@@ -110,7 +110,7 @@ Fréchet Inception Distance (FID) of the **MAISI-v2** baseline and **NoMAISI** m
 ### 📉 FID Parity Plot
 
 <p align="left">
-  <img src="https://github.com/fitushar/NoMAISI/blob/main/doc/images/GanAI_fid_scatter_marker_legend.png" alt="Parity comparison of FID for real↔real vs AI-generated CT across datasets" width="500">
+  <img src="doc/images/GanAI_fid_scatter_marker_legend.png" alt="Parity comparison of FID for real↔real vs AI-generated CT across datasets" width="500">
 </p>
 
 **Comparison of Fréchet Inception Distance (FID) between real↔real and AI-generated CT datasets.**   Each point represents a clinical dataset (**LNDbv4, NSCLC-R, LIDC-IDRI, DLCS24, Intgmultiomics, LUNA25**) under different generative models (**MAISI-V2, NoMAISI**).The x-axis shows the **median FID** computed between real datasets, while the y-axis shows the **FID of AI-generated data** compared to real.  
@@ -124,13 +124,13 @@ The dashed diagonal line denotes **parity (y = x)**, where AI-generated fidelity
 - **Yellow boxes** highlight lung nodule regions for comparison.
 
 <p align="center">
-  <img src="https://github.com/fitushar/NoMAISI/blob/main/doc/images/DLCS_1419_ann0_slice134_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
+  <img src="doc/images/DLCS_1419_ann0_slice134_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
 </p>
 <p align="center">
-  <img src="https://github.com/fitushar/NoMAISI/blob/main/doc/images/DLCS_1508_ann0_slice46_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
+  <img src="doc/images/DLCS_1508_ann0_slice46_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
 </p>
 <p align="center">
-  <img src="https://github.com/fitushar/NoMAISI/blob/main/doc/images/DLCS_1453_ann0_slice204_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
+  <img src="doc/images/DLCS_1453_ann0_slice204_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
 </p>
 
 
@@ -204,7 +204,7 @@ python -m scripts.infer_testV2_controlnet \
 
 ## 🔬 Downstream Task: Cancer vs. No-Cancer Classification
 
-![Cancer/No-Cancer Classification Results](https://github.com/fitushar/NoMAISI/blob/main/doc/images/TaskCls.png)
+![Cancer/No-Cancer Classification Results](doc/images/TaskCls.png)
 
 **Shown.** AUC vs. the **% of clinical data retained** (x-axis: **100%**, **50%**, **20%**, **10%**).
 **Curves (additive augmentation — we **add** AI-generated nodules; we never replace clinical samples):**