RACR-MIL: Weakly Supervised Skin Cancer Grading using Rank-Aware Contextual Reasoning on Whole Slide Images

• URL: http://arxiv.org/abs/2308.15618v1
• Date: Tue, 29 Aug 2023 20:25:49 GMT
• Title: RACR-MIL: Weakly Supervised Skin Cancer Grading using Rank-Aware Contextual Reasoning on Whole Slide Images
• Authors: Anirudh Choudhary, Angelina Hwang, Jacob Kechter, Krishnakant Saboo, Blake Bordeaux, Puneet Bhullar, Nneka Comfere, David DiCaudo, Steven Nelson, Emma Johnson, Leah Swanson, Dennis Murphree, Aaron Mangold, Ravishankar K. Iyer
• Abstract summary: Cutaneous squamous cell cancer (cSCC) is the second most common skin cancer in the US. We propose an automated weakly-supervised grading approach for c SCC WSIs. The proposed model transforms each WSI into a bag of tiled patches and leverages attention-based multiple-instance learning to assign a WSI-level grade.
• Score: 1.7625232415232948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cutaneous squamous cell cancer (cSCC) is the second most common skin cancer in the US. It is diagnosed by manual multi-class tumor grading using a tissue whole slide image (WSI), which is subjective and suffers from inter-pathologist variability. We propose an automated weakly-supervised grading approach for cSCC WSIs that is trained using WSI-level grade and does not require fine-grained tumor annotations. The proposed model, RACR-MIL, transforms each WSI into a bag of tiled patches and leverages attention-based multiple-instance learning to assign a WSI-level grade. We propose three key innovations to address general as well as cSCC-specific challenges in tumor grading. First, we leverage spatial and semantic proximity to define a WSI graph that encodes both local and non-local dependencies between tumor regions and leverage graph attention convolution to derive contextual patch features. Second, we introduce a novel ordinal ranking constraint on the patch attention network to ensure that higher-grade tumor regions are assigned higher attention. Third, we use tumor depth as an auxiliary task to improve grade classification in a multitask learning framework. RACR-MIL achieves 2-9% improvement in grade classification over existing weakly-supervised approaches on a dataset of 718 cSCC tissue images and localizes the tumor better. The model achieves 5-20% higher accuracy in difficult-to-classify high-risk grade classes and is robust to class imbalance.

Related papers

• MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
  We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation.<n>Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning.<n>Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
  arXiv  Detail & Related papers  (2025-03-18T15:39:44Z)
• Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification [50.899861205016265]
  We propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification. Our framework introduces two key components into the common MIL model architecture. We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets.
  arXiv  Detail & Related papers  (2025-03-08T04:51:58Z)
• SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing [17.798774864007505]
  Liver cancer is a leading cause of mortality worldwide. precise liver tumor segmentation remains challenging due to the heterogeneous nature of tumors. We present SmoothSegNet, a novel deep learning framework that addresses these challenges with the three key designs.
  arXiv  Detail & Related papers  (2024-10-13T20:52:25Z)
• Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation [71.91773485443125]
  We show that the best AUC is achieved by the CDI$s$ - optimized modality, outperforming the best gold-standard modality by 0.0044. Notably, the optimized CDI$s$ modality also achieves AUC values over 0.02 higher than the Unoptimized CDI$s$ value.
  arXiv  Detail & Related papers  (2024-05-13T16:07:58Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• 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)
• Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images [3.7282630026096597]
  Cross-attention-based salient instance inference MIL (CASiiMIL) is proposed to identify breast cancer lymph node micro-metastasis on whole slide images. We introduce a negative representation learning algorithm to facilitate the learning of saliency-informed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-of-the-art MIL methods on two popular tumor metastasis detection datasets.
  arXiv  Detail & Related papers  (2023-09-18T00:56:19Z)
• 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)
• Context-Aware Self-Supervised Learning of Whole Slide Images [0.0]
  A novel two-stage learning technique is presented in this work. A graph representation capturing all dependencies among regions in the WSI is very intuitive. The entire slide is presented as a graph, where the nodes correspond to the patches from the WSI. The proposed framework is then tested using WSIs from prostate and kidney cancers.
  arXiv  Detail & Related papers  (2023-06-07T20:23:05Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning [48.02011627390706]
  We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.
  arXiv  Detail & Related papers  (2023-03-02T15:18:58Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)
• Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images [22.128902125820193]
  We propose a Decoupled Pyramid Correlation Network (DPC-Net) It exploits attention mechanisms to fully leverage both low and high-level features embedded in FCN to segment liver tumor. It achieves a competitive results with a DSC of 96.2% and an ASSD of 1.636 mm for liver segmentation.
  arXiv  Detail & Related papers  (2022-05-26T07:31:29Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers [0.0]
  Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC) Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. We leverage recent advances in self-supervised learning by training neural networks on histology images from the TCGA dataset using MoCo V2.
  arXiv  Detail & Related papers  (2021-09-13T09:43:12Z)
• A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images [1.244681179922733]
  We present a self-learning framework to grade bladder cancer from histological images stained viachemical techniques. We propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows classifying histological patches into different levels of the disease.
  arXiv  Detail & Related papers  (2021-06-25T11:04:04Z)
• Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms [0.4297070083645048]
  We present a novel generative adversarial network (GAN) model for data augmentation that can realistically synthesize and remove lesions on mammograms. With self-attention and semi-supervised learning components, the U-net-based architecture can generate high resolution (256x256px) outputs.
  arXiv  Detail & Related papers  (2020-05-29T21:23:00Z)
• A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms [13.842620686759616]
  Mammograms are commonly employed in the large scale screening of breast cancer. We propose a two-stage Multiple Instance Learning framework for image-level detection of malignancy. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the imagelevel classification task.
  arXiv  Detail & Related papers  (2020-04-24T13:06:47Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)
• Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images [16.02231907106384]
  We develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks. The classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists.
  arXiv  Detail & Related papers  (2020-01-06T14:09:51Z)

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.