Synthetic-to-Real Transfer Learning for Chromatin-Sensitive PWS Microscopy

• URL: http://arxiv.org/abs/2510.22239v1
• Date: Sat, 25 Oct 2025 10:00:34 GMT
• Title: Synthetic-to-Real Transfer Learning for Chromatin-Sensitive PWS Microscopy
• Authors: Jahidul Arafat, Sanjaya Poudel,
• Abstract summary: We present CFU Net, a hierarchical segmentation architecture trained with a three stage curriculum on synthetic CFU data.<n>Our approach uses physics based rendering that incorporates empirically supported packing statistics, Mie scattering models, and modality specific noise, combined with a curriculum that progresses from adversarial pretraining to spectroscopic fine tuning and histology validation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chromatin sensitive partial wave spectroscopic (csPWS) microscopy enables label free detection of nanoscale chromatin packing alterations that occur before visible cellular transformation. However, manual nuclear segmentation limits population scale analysis needed for biomarker discovery in early cancer detection. The lack of annotated csPWS imaging data prevents direct use of standard deep learning methods. We present CFU Net, a hierarchical segmentation architecture trained with a three stage curriculum on synthetic multimodal data. CFU Net achieves near perfect performance on held out synthetic test data that represent diverse spectroscopic imaging conditions without manual annotations (Dice 0.9879, IoU 0.9895). Our approach uses physics based rendering that incorporates empirically supported chromatin packing statistics, Mie scattering models, and modality specific noise, combined with a curriculum that progresses from adversarial RGB pretraining to spectroscopic fine tuning and histology validation. CFU Net integrates five architectural elements (ConvNeXt backbone, Feature Pyramid Network, UNet plus plus dense connections, dual attention, and deep supervision) that together improve Dice over a baseline UNet by 8.3 percent. We demonstrate deployment ready INT8 quantization with 74.9 percent compression and 0.15 second inference, giving a 240 times throughput gain over manual analysis. Applied to more than ten thousand automatically segmented nuclei from synthetic test data, the pipeline extracts chromatin biomarkers that distinguish normal from pre cancerous tissue with large effect sizes (Cohens d between 1.31 and 2.98), reaching 94 percent classification accuracy. This work provides a general framework for synthetic to real transfer learning in specialized microscopy and open resources for community validation on clinical specimens.

Related papers

• A Machine Vision Approach to Preliminary Skin Lesion Assessments [0.0]
  This study evaluates a comprehensive system for preliminary skin lesion assessment that combines the clinically established ABCD rule of dermoscopy with machine learning classification.<n>A custom three-layer Convolutional Neural Network (CNN) trained from scratch achieved 78.5% accuracy and 86.5% recall on median-filtered images.
  arXiv  Detail & Related papers  (2026-01-21T23:48:59Z)
• SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis [0.0]
  generative architecture choice has a stronger influence on both image fidelity and diagnostic utility than preprocessing complexity.<n>Synthetic data augmentation substantially improved melanoma detection with 8-15% absolute gains in melanoma F1-score.
  arXiv  Detail & Related papers  (2025-12-19T13:52:11Z)
• Cancer-Net PCa-MultiSeg: Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging [55.62977326180104]
  Current deep learning approaches for prostate cancer lesion segmentation achieve limited performance.<n>We investigate synthetic correlated diffusion imaging (CDI$s$) as an enhancement to standard diffusion-based protocols.<n>Our results establish validated integration pathways for CDI$s$ as a practical drop-in enhancement for PCa lesion segmentation tasks.
  arXiv  Detail & Related papers  (2025-11-11T04:16:12Z)
• Skin Cancer Classification: Hybrid CNN-Transformer Models with KAN-Based Fusion [0.0]
  We explore Sequential and Parallel Hybrid CNN-Transformer models with Convolutional Kolmogorov-Arnold Network (CKAN)<n>Our approach integrates transfer learning and extensive data augmentation, where CNNs extract local spatial features, Transformers model global dependencies, and CKAN facilitates nonlinear feature fusion for improved representation learning.<n>Our proposed approach achieves competitive performance in skin cancer classification, demonstrating 92.81% accuracy and 92.47% F1-score on the HAM10000 dataset, 97.83% accuracy and 97.83% F1-score on the PAD-UFES dataset, and 91.17% accuracy with 91.79% F1- score on
  arXiv  Detail & Related papers  (2025-08-17T19:57:34Z)
• HistoART: Histopathology Artifact Detection and Reporting Tool [37.31105955164019]
  Whole Slide Imaging (WSI) is widely used to digitize tissue specimens for detailed, high-resolution examination.<n>WSI remains vulnerable to artifacts introduced during slide preparation and scanning.<n>We propose and compare three robust artifact detection approaches for WSIs.
  arXiv  Detail & Related papers  (2025-06-23T17:22:19Z)
• Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images [0.0]
  This work presents a hybrid machine and deep learning-based approach for classifying skin lesions.<n>It comprises 401,059 cropped lesion images extracted from 3D Total Body Photography (TBP), emulating non-dermoscopic, smartphone-like conditions.<n>Predictions are fused with a gradient-boosted decision tree (GBDT) ensemble enriched by engineered features and patient-specific relational metrics.
  arXiv  Detail & Related papers  (2025-06-03T22:00:03Z)
• Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data [36.92842246372894]
  Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN) is a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples.<n>By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates robust predictive performance and interpretability.
  arXiv  Detail & Related papers  (2025-03-29T02:14:05Z)
• A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver [0.0]
  Hyperspectral imaging holds significant potential for transforming the field of computational pathology. There is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. We present a database consisted of 27 HSIs of hematoxylin-eosin stained frozen sections, collected from 14 patients with colon adenocarcinoma metastasized to the liver.
  arXiv  Detail & Related papers  (2024-11-11T13:17:55Z)
• Multimodal Prototyping for cancer survival prediction [45.61869793509184]
  Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification. Current approaches involve tokenizing the WSIs into smaller patches (>10,000 patches) and transcriptomics into gene groups, which are then integrated using a Transformer for predicting outcomes. This process generates many tokens, which leads to high memory requirements for computing attention and complicates post-hoc interpretability analyses. Our framework outperforms state-of-the-art methods with much less computation while unlocking new interpretability analyses.
  arXiv  Detail & Related papers  (2024-06-28T20:37:01Z)
• Interpretable cancer cell detection with phonon microscopy using multi-task conditional neural networks for inter-batch calibration [39.759100498329275]
  We present a conditional neural network framework to simultaneously achieve inter-batch calibration. We validate our approach by training and validating on different experimental batches. We extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state.
  arXiv  Detail & Related papers  (2024-03-26T12:20:10Z)
• CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images [42.771819949806655]
  We introduce CIMIL-CRC', a framework that solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort.
  arXiv  Detail & Related papers  (2024-01-29T12:56:11Z)
• Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation [3.3148826359547523]
  This study aims to develop a deep learning framework for generating synthetic lesions that can be used to enhance network training. The lesion synthesis network is a modified generative adversarial network (GAN) The discriminator is designed using Wasserstein GAN with gradient penalty and spectral normalization.
  arXiv  Detail & Related papers  (2022-06-18T00:40:41Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.