Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques

Breast cancer remains a critical global health challenge, necessitating early and accurate detection for effective treatment. This paper introduces a methodology that combines automated image augmentation selection (RandAugment) with search optimisation strategies (Tree-based Parzen Estimator) to identify optimal values for the number of image augmentations and the magnitude of their associated augmentation parameters, leading to enhanced segmentation performance. We empirically validate our approach on breast cancer histology slides, focusing on the segmentation of cancer cells. A comparative analysis of state-of-the-art transformer-based segmentation models is conducted, including SegFormer, PoolFormer, and MaskFormer models, to establish a comprehensive baseline, before applying the augmentation methodology. Our results show that the proposed methodology leads to segmentation models that are more resilient to variations in histology slides whilst maintaining high levels of segmentation performance, and show improved segmentation of the tumour class when compared to previous research. Our best result after applying the augmentations is a Dice Score of 84.08 and an IoU score of 72.54 when segmenting the tumour class. The primary contribution of this paper is the development of a methodology that enhances segmentation performance while ensuring model robustness to data variances. This has significant implications for medical practitioners, enabling the development of more effective machine learning models for clinical applications to identify breast cancer cells from histology slides. Furthermore, the codebase accompanying this research will be released upon publication. This will facilitate further research and application development based on our methodology, thereby amplifying its impact.

Virchow: A Million-Slide Digital Pathology Foundation Model

Computational pathology uses artificial intelligence to enable precision medicine and decision support systems through the analysis of whole slide images. It has the potential to revolutionize the diagnosis and treatment of cancer. However, a major challenge to this objective is that for many specific computational pathology tasks the amount of data is inadequate for development. To address this challenge, we created Virchow, a 632 million parameter deep neural network foundation model for computational pathology. Using self-supervised learning, Virchow is trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue groups, which is orders of magnitude more data than previous works. When evaluated on downstream tasks including tile-level pan-cancer detection and subtyping and slide-level biomarker prediction, Virchow outperforms state-of-the-art systems both on internal datasets drawn from the same population as the pretraining data as well as external public datasets. Virchow achieves 93% balanced accuracy for pancancer tile classification, and AUCs of 0.983 for colon microsatellite instability status prediction and 0.967 for breast CDH1 status prediction. The gains in performance highlight the importance of pretraining on massive pathology image datasets, suggesting pretraining on even larger datasets could continue improving performance for many high-impact applications where limited amounts of training data are available, such as drug outcome prediction.

Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores

This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images. The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Net-based architectures, augmented with Monte-Carlo dropout, are evaluated for automatic segmentation of the central zone, peripheral zone, transition zone, and tumor, with uncertainty estimation. The top-performing model in this study is the Attention R2U-Net, achieving a mean Intersection over Union (IoU) of 76.3% and Dice Similarity Coefficient (DSC) of 85% for segmenting all zones. Additionally, Attention R2U-Net exhibits the lowest uncertainty values, particularly in the boundaries of the transition zone and tumor, when compared to the other models.

Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images

Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promising technology in computer vision, has emerged as a potential solution to improve the accuracy and efficiency of cervical cancer screening compared to traditional clinical inspection methods that are prone to human error. This review article discusses cervical cancer and its screening processes, followed by the Deep Learning training process and the classification, segmentation, and detection tasks for cervical cancer diagnosis. Additionally, we explored the most common public datasets used in both cytology and colposcopy and highlighted the popular and most utilized architectures that researchers have applied to both cytology and colposcopy. We reviewed 24 selected practical papers in this study and summarized them. This article highlights the remarkable efficiency in enhancing the precision and speed of cervical cancer analysis by Deep Learning, bringing us closer to early diagnosis and saving lives.

Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation

Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women. Although machine learning-based Computer-Aided Diagnosis (CAD) systems have shown potential to assist radiologists in analyzing medical images, the opaque nature of the best-performing CAD systems has raised concerns about their trustworthiness and interpretability. This paper proposes MT-BI-RADS, a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. The approach offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy. Firstly, the proposed model outputs the BI-RADS categories used for BUS image analysis by radiologists. Secondly, the model employs multi-task learning to concurrently segment regions in images that correspond to tumors. Thirdly, the proposed approach outputs quantified contributions of each BI-RADS descriptor toward predicting the benign or malignant class using post-hoc explanations with Shapley Values.

Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients

Estimating the uncertainty of deep learning models in a reliable and efficient way has remained an open problem, where many different solutions have been proposed in the literature. Most common methods are based on Bayesian approximations, like Monte Carlo dropout (MCDO) or Deep ensembling (DE), but they have a high inference time (i.e. require multiple inference passes) and might not work for out-of-distribution detection (OOD) data (i.e. similar uncertainty for in-distribution (ID) and OOD). In safety critical environments, like medical applications, accurate and fast uncertainty estimation methods, able to detect OOD data, are crucial, since wrong predictions can jeopardize patients safety. In this study, we present an alternative direct uncertainty estimation method and apply it for a regression U-Net architecture. The method consists in the addition of a branch from the bottleneck which reconstructs the input. The input reconstruction error can be used as a surrogate of the model uncertainty. For the proof-of-concept, our method is applied to proton therapy dose prediction in head and neck cancer patients. Accuracy, time-gain, and OOD detection are analyzed for our method in this particular application and compared with the popular MCDO and DE. The input reconstruction method showed a higher Pearson correlation coefficient with the prediction error (0.620) than DE and MCDO (between 0.447 and 0.612). Moreover, our method allows an easier identification of OOD (Z-score of 34.05). It estimates the uncertainty simultaneously to the regression task, therefore requires less time or computational resources.

Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection

Background: Prostate cancer (PC) MRI-based risk calculators are commonly based on biological (e.g. PSA), MRI markers (e.g. volume), and patient age. Whilst patient age measures the amount of years an individual has existed, biological age (BA) might better reflect the physiology of an individual. However, surrogates from prostate MRI and linkage with clinically significant PC (csPC) remain to be explored. Purpose: To obtain and evaluate Prostate Age Gap (PAG) as an MRI marker tool for csPC risk. Study type: Retrospective. Population: A total of 7243 prostate MRI slices from 468 participants who had undergone prostate biopsies. A deep learning model was trained on 3223 MRI slices cropped around the gland from 81 low-grade PC (ncsPC, Gleason score <=6) and 131 negative cases and tested on the remaining 256 participants. Assessment: Chronological age was defined as the age of the participant at the time of the visit and used to train the deep learning model to predict the age of the patient. Following, we obtained PAG, defined as the model predicted age minus the patient's chronological age. Multivariate logistic regression models were used to estimate the association through odds ratio (OR) and predictive value of PAG and compared against PSA levels and PI-RADS>=3. Statistical tests: T-test, Mann-Whitney U test, Permutation test and ROC curve analysis. Results: The multivariate adjusted model showed a significant difference in the odds of clinically significant PC (csPC, Gleason score >=7) (OR =3.78, 95% confidence interval (CI):2.32-6.16, P <.001). PAG showed a better predictive ability when compared to PI-RADS>=3 and adjusted by other risk factors, including PSA levels: AUC =0.981 vs AUC =0.704, p<.001. Conclusion: PAG was significantly associated with the risk of clinically significant PC and outperformed other well-established PC risk factors.

Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. A mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimised using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. Performance evaluation, leveraging multiple metrics, is conducted, and a comparative analysis against conventional methods is presented. Our experimental findings reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings

Automatic methods for early detection of breast cancer on mammography can significantly decrease mortality. Broad uptake of those methods in hospitals is currently hindered because the methods have too many constraints. They assume annotations available for single images or even regions-of-interest (ROIs), and a fixed number of images per patient. Both assumptions do not hold in a general hospital setting. Relaxing those assumptions results in a weakly supervised learning setting, where labels are available per case, but not for individual images or ROIs. Not all images taken for a patient contain malignant regions and the malignant ROIs cover only a tiny part of an image, whereas most image regions represent benign tissue. In this work, we investigate a two-level multi-instance learning (MIL) approach for case-level breast cancer prediction on two public datasets (1.6k and 5k cases) and an in-house dataset of 21k cases. Observing that breast cancer is usually only present in one side, while images of both breasts are taken as a precaution, we propose a domain-specific MIL pooling variant. We show that two-level MIL can be applied in realistic clinical settings where only case labels, and a variable number of images per patient are available. Data in realistic settings scales with continuous patient intake, while manual annotation efforts do not. Hence, research should focus in particular on unsupervised ROI extraction, in order to improve breast cancer prediction for all patients.