Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification

The past decade has witnessed a substantial increase in the number of startups and companies offering AI-based solutions for clinical decision support in medical institutions. However, the critical nature of medical decision-making raises several concerns about relying on external software. Key issues include potential variations in image modalities and the medical devices used to obtain these images, potential legal issues, and adversarial attacks. Fortunately, the open-source nature of machine learning research has made foundation models publicly available and straightforward to use for medical applications. This accessibility allows medical institutions to train their own AI-based models, thereby mitigating the aforementioned concerns. Given this context, an important question arises: how much data do medical institutions need to train effective AI models? In this study, we explore this question in relation to breast cancer detection, a particularly contested area due to the prevalence of this disease, which affects approximately 1 in every 8 women. Through large-scale experiments on various patient sizes in the training set, we show that medical institutions do not need a decade's worth of MRI images to train an AI model that performs competitively with the state-of-the-art, provided the model leverages foundation models. Furthermore, we observe that for patient counts greater than 50, the number of patients in the training set has a negligible impact on the performance of models and that simple ensembles further improve the results without additional complexity.

TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound

While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. There is high heterogeneity in tissue appearance, significant class imbalance in favor of benign examples, and scarcity in the number and quality of ground truth annotations available to train models. Failure to address even a single one of these problems can result in unacceptable clinical outcomes.We propose TRUSWorthy, a carefully designed, tuned, and integrated system for reliable PCa detection. Our pipeline integrates self-supervised learning, multiple-instance learning aggregation using transformers, random-undersampled boosting and ensembling: these address label scarcity, weak labels, class imbalance, and overconfidence, respectively. We train and rigorously evaluate our method using a large, multi-center dataset of micro-ultrasound data. Our method outperforms previous state-of-the-art deep learning methods in terms of accuracy and uncertainty calibration, with AUROC and balanced accuracy scores of 79.9% and 71.5%, respectively. On the top 20% of predictions with the highest confidence, we can achieve a balanced accuracy of up to 91%. The success of TRUSWorthy demonstrates the potential of integrated deep learning solutions to meet clinical needs in a highly challenging deployment setting, and is a significant step towards creating a trustworthy system for computer-assisted PCa diagnosis.

SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis

Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse prognostic factors and complex pathological features. Pathologists currently rely on time consuming manual assessments, which are highly subjective and prone to variation. This highlights the urgent need for automated and precise diag nostic solutions. 2,970 lung cancer tissue slides are comprised from multiple centers, re-diagnosed them, and constructed and publicly released three lung cancer STAS datasets: STAS CSU (hospital), STAS TCGA, and STAS CPTAC. All STAS datasets provide corresponding pathological feature diagnoses and related clinical data. To address the bias, sparse and heterogeneous nature of STAS, we propose an scale-aware multiple instance learning(SMILE) method for STAS diagnosis of lung cancer. By introducing a scale-adaptive attention mechanism, the SMILE can adaptively adjust high attention instances, reducing over-reliance on local regions and promoting consistent detection of STAS lesions. Extensive experiments show that SMILE achieved competitive diagnostic results on STAS CSU, diagnosing 251 and 319 STAS samples in CPTAC andTCGA,respectively, surpassing clinical average AUC. The 11 open baseline results are the first to be established for STAS research, laying the foundation for the future expansion, interpretability, and clinical integration of computational pathology technologies. The datasets and code are available at https://anonymous.4open.science/r/IJCAI25-1DA1.

SCFANet: Style Distribution Constraint Feature Alignment Network For Pathological Staining Translation

Immunohistochemical (IHC) staining serves as a valuable technique for detecting specific antigens or proteins through antibody-mediated visualization. However, the IHC staining process is both time-consuming and costly. To address these limitations, the application of deep learning models for direct translation of cost-effective Hematoxylin and Eosin (H&E) stained images into IHC stained images has emerged as an efficient solution. Nevertheless, the conversion from H&E to IHC images presents significant challenges, primarily due to alignment discrepancies between image pairs and the inherent diversity in IHC staining style patterns. To overcome these challenges, we propose the Style Distribution Constraint Feature Alignment Network (SCFANet), which incorporates two innovative modules: the Style Distribution Constrainer (SDC) and Feature Alignment Learning (FAL). The SDC ensures consistency between the generated and target images' style distributions while integrating cycle consistency loss to maintain structural consistency. To mitigate the complexity of direct image-to-image translation, the FAL module decomposes the end-to-end translation task into two subtasks: image reconstruction and feature alignment. Furthermore, we ensure pathological consistency between generated and target images by maintaining pathological pattern consistency and Optical Density (OD) uniformity. Extensive experiments conducted on the Breast Cancer Immunohistochemical (BCI) dataset demonstrate that our SCFANet model outperforms existing methods, achieving precise transformation of H&E-stained images into their IHC-stained counterparts. The proposed approach not only addresses the technical challenges in H&E to IHC image translation but also provides a robust framework for accurate and efficient stain conversion in pathological analysis.

From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans

Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging, treatment planning and beyond. Identifying scatteredly-distributed and low-contrast LNs in 3D CT scans is highly challenging, even for experienced clinicians. Previous lesion and LN detection methods demonstrate effectiveness of 2.5D approaches (i.e, using 2D network with multi-slice inputs), leveraging pretrained 2D model weights and showing improved accuracy as compared to separate 2D or 3D detectors. However, slice-based 2.5D detectors do not explicitly model inter-slice consistency for LN as a 3D object, requiring heuristic post-merging steps to generate final 3D LN instances, which can involve tuning a set of parameters for each dataset. In this work, we formulate 3D LN detection as a tracking task and propose LN-Tracker, a novel LN tracking transformer, for joint end-to-end detection and 3D instance association. Built upon DETR-based detector, LN-Tracker decouples transformer decoder's query into the track and detection groups, where the track query autoregressively follows previously tracked LN instances along the z-axis of a CT scan. We design a new transformer decoder with masked attention module to align track query's content to the context of current slice, meanwhile preserving detection query's high accuracy in current slice. An inter-slice similarity loss is introduced to encourage cohesive LN association between slices. Extensive evaluation on four lymph node datasets shows LN-Tracker's superior performance, with at least 2.7% gain in average sensitivity when compared to other top 3D/2.5D detectors. Further validation on public lung nodule and prostate tumor detection tasks confirms the generalizability of LN-Tracker as it achieves top performance on both tasks. Datasets will be released upon acceptance.

"No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images

Accurate tumor detection in digital pathology whole-slide images (WSIs) is crucial for cancer diagnosis and treatment planning. Multiple Instance Learning (MIL) has emerged as a widely used approach for weakly-supervised tumor detection with large-scale data without the need for manual annotations. However, traditional MIL methods often depend on classification tasks that require tumor-free cases as negative examples, which are challenging to obtain in real-world clinical workflows, especially for surgical resection specimens. We address this limitation by reformulating tumor detection as a regression task, estimating tumor percentages from WSIs, a clinically available target across multiple cancer types. In this paper, we provide an analysis of the proposed weakly-supervised regression framework by applying it to multiple organs, specimen types and clinical scenarios. We characterize the robustness of our framework to tumor percentage as a noisy regression target, and introduce a novel concept of amplification technique to improve tumor detection sensitivity when learning from small tumor regions. Finally, we provide interpretable insights into the model's predictions by analyzing visual attention and logit maps. Our code is available at https://github.com/DIAGNijmegen/tumor-percentage-mil-regression.

Robust Polyp Detection and Diagnosis through Compositional Prompt-Guided Diffusion Models

Colorectal cancer (CRC) is a significant global health concern, and early detection through screening plays a critical role in reducing mortality. While deep learning models have shown promise in improving polyp detection, classification, and segmentation, their generalization across diverse clinical environments, particularly with out-of-distribution (OOD) data, remains a challenge. Multi-center datasets like PolypGen have been developed to address these issues, but their collection is costly and time-consuming. Traditional data augmentation techniques provide limited variability, failing to capture the complexity of medical images. Diffusion models have emerged as a promising solution for generating synthetic polyp images, but the image generation process in current models mainly relies on segmentation masks as the condition, limiting their ability to capture the full clinical context. To overcome these limitations, we propose a Progressive Spectrum Diffusion Model (PSDM) that integrates diverse clinical annotations-such as segmentation masks, bounding boxes, and colonoscopy reports-by transforming them into compositional prompts. These prompts are organized into coarse and fine components, allowing the model to capture both broad spatial structures and fine details, generating clinically accurate synthetic images. By augmenting training data with PSDM-generated samples, our model significantly improves polyp detection, classification, and segmentation. For instance, on the PolypGen dataset, PSDM increases the F1 score by 2.12% and the mean average precision by 3.09%, demonstrating superior performance in OOD scenarios and enhanced generalization.

Automatic Prostate Volume Estimation in Transabdominal Ultrasound Images

Prostate cancer is a leading health concern among men, requiring accurate and accessible methods for early detection and risk stratification. Prostate volume (PV) is a key parameter in multivariate risk stratification for early prostate cancer detection, commonly estimated using transrectal ultrasound (TRUS). While TRUS provides precise prostate volume measurements, its invasive nature often compromises patient comfort. Transabdominal ultrasound (TAUS) provides a non-invasive alternative but faces challenges such as lower image quality, complex interpretation, and reliance on operator expertise. This study introduces a new deep-learning-based framework for automatic PV estimation using TAUS, emphasizing its potential to enable accurate and non-invasive prostate cancer risk stratification. A dataset of TAUS videos from 100 individual patients was curated, with manually delineated prostate boundaries and calculated diameters by an expert clinician as ground truth. The introduced framework integrates deep-learning models for prostate segmentation in both axial and sagittal planes, automatic prostate diameter estimation, and PV calculation. Segmentation performance was evaluated using Dice correlation coefficient (%) and Hausdorff distance (mm). Framework's volume estimation capabilities were evaluated on volumetric error (mL). The framework demonstrates that it can estimate PV from TAUS videos with a mean volumetric error of -5.5 mL, which results in an average relative error between 5 and 15%. The introduced framework for automatic PV estimation from TAUS images, utilizing deep learning models for prostate segmentation, shows promising results. It effectively segments the prostate and estimates its volume, offering potential for reliable, non-invasive risk stratification for early prostate detection.

Lung Cancer detection using Deep Learning

In this paper we discuss lung cancer detection using hybrid model of Convolutional-Neural-Networks (CNNs) and Support-Vector-Machines-(SVMs) in order to gain early detection of tumors, benign or malignant. The work uses this hybrid model by training upon the Computed Tomography scans (CT scans) as dataset. Using deep learning for detecting lung cancer early is a cutting-edge method.

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors

Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformers architectures, including Swin Transformer and MaxViT, in several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset includes different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual image modalities. The results revealed that the Swin Transformer provided high accuracy, achieving up to 99.9\% for kidney tumor classification and 99.3\% accuracy in a combined dataset. MaxViT also provided excellent results in individual datasets but performed poorly when data is combined. This research highlights the adaptability of Transformer-based models to various image modalities and features. However, challenges persist, including limited annotated data and interpretability issues. Future works will expand this study by incorporating other image modalities and enhancing diagnostic capabilities. Integrating these models across diverse datasets could mark a pivotal advance in precision medicine, paving the way for more efficient and comprehensive healthcare solutions.