DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture

• URL: http://arxiv.org/abs/2305.01968v1
• Date: Wed, 3 May 2023 08:31:44 GMT
• Title: DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
• Abstract summary: In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs) We developed a novel and efficient digital pathology classifier called DPSeq, to predict cancer biomarkers.
• Score: 4.876281217951695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. In this study, we developed a novel and efficient digital pathology classifier called DPSeq, to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizon and vertical bidirectional long short-term memory (BiLSTM) networks. Using hematoxylin and eosin (H&E)-stained histopathological images of colorectal cancer (CRC) from two international datasets: The Cancer Genome Atlas (TCGA) and Molecular and Cellular Oncology (MCO), the predictive performance of DPSeq was evaluated in series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in CRC (MSI status, Hypermutation, CIMP status, BRAF mutation, TP53 mutation and chromosomal instability [CING]), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status. Furthermore, DPSeq required less time for both training and prediction due to its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.

Related papers

• Benchmarking CNN-based Models against Transformer-based Models for Abdominal Multi-Organ Segmentation on the RATIC Dataset [3.428885759047836]
  Multi-organ segmentation in abdominal CT scans is essential for computer-aided diagnosis and treatment.<n> transformer-based architectures have recently gained attention due to their ability to model long-range dependencies.<n>In this study, we benchmark the three hybrid transformer-based models UNETR, SwinUNETR, and UNETR++ against a strong CNN baseline, SegResNet, for volumetric multi-organ segmentation.
  arXiv  Detail & Related papers  (2026-03-19T08:35:11Z)
• Data Augmentation for High-Fidelity Generation of CAR-T/NK Immunological Synapse Images [40.26649380711007]
  Chimeric antigen receptor (CAR)-T and NK cell immunotherapies have transformed cancer treatment.<n>The quality of the CAR-T/NK cell immunological synapse (IS) may serve as a functional biomarker for predicting therapeutic efficacy.
  arXiv  Detail & Related papers  (2026-02-01T00:54:53Z)
• Stage-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI [1.1458853556386799]
  We present the first stage-specific, cross-sectional benchmarking of deep learning models for follow-up MRI.<n>We analyze different post-RT scans independently to test whether architecture performance depends on time-point.<n>These results establish a stage-aware benchmark and motivate future work incorporating longitudinal modeling, multi-sequence MRI, and larger multi-center cohorts.
  arXiv  Detail & Related papers  (2025-11-23T19:38:03Z)
• Multi-Scale Deep Learning for Colon Histopathology: A Hybrid Graph-Transformer Approach [0.0]
  Colon cancer also known as Colorectal cancer, is one of the most malignant types of cancer worldwide.<n>This research presents a hybrid multi-scale deep learning architecture that synergizes capsule networks, graph attention mechanisms, transformer modules, and residual learning to advance colon cancer classification.
  arXiv  Detail & Related papers  (2025-09-02T21:40:57Z)
• GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis [44.99833362998488]
  We present a novel approach that combines 2D Gaussian splatting with the Transformer UNet architecture for automated skin cancer diagnosis.<n>Our findings illustrate significant advancements in the precision of segmentation and classification.<n>This integration sets new benchmarks in the field and highlights the potential for further research into multi-task medical image analysis methodologies.
  arXiv  Detail & Related papers  (2025-02-23T23:28:47Z)
• Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images [37.92866910969225]
  We propose a novel hip landmark detection model by integrating the Topological GCN (TGCN) with an Improved Conformer (TGCN-ICF) The TGCN-ICF includes two-works: an Improved Conformer (ICF) subnetwork to generate heatmaps and a TGCN subnetwork to additionally refine landmark detection. The experimental results on the real DDH dataset demonstrate that the proposed TGCN-ICF outperforms all the compared algorithms.
  arXiv  Detail & Related papers  (2024-08-24T06:47:38Z)
• Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
  We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-11T15:46:00Z)
• 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)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines [4.876281217951695]
  NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. We developed digital pathology pipelines to benchmark the five most recently proposed NLP models and four popular CNN models. Our NLP models achieved state-of-the-art predictions for all three biomarkers using a relatively small training dataset.
  arXiv  Detail & Related papers  (2023-02-21T02:42:03Z)
• Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer [3.6695403836792493]
  We developed an efficient workflow for biomarkers in colorectal cancers using Shifted Windows (Swin-T) Swin-T was extremely efficient using small training datasets and exhibits robust predictive performance with only 200-500 training samples. These data indicate that Swin-T may be 5-10 times more efficient than the current state-of-the-art algorithms for MSI.
  arXiv  Detail & Related papers  (2022-08-22T02:32:30Z)
• A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations [4.674211520843232]
  We propose a novel Attention-based Multiple Instance Mutation Learning (AMIML) model for predicting gene mutations. AMIML was comprised of successive 1-D convolutional layers, a decoder, and a residual weight connection to facilitate further integration of a lightweight attention mechanism. AMIML demonstrated excellent robustness, not only outperforming all the five baseline algorithms in the vast majority of the tested genes, but also providing near-best-performance for the other seven genes.
  arXiv  Detail & Related papers  (2022-05-31T15:45:29Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

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.