# Breast Cancer Classification with Enhanced Interpretability: DALAResNet50 and DT Grad-CAM

arXiv: https://arxiv.org/abs/2308.13150 · HTML: https://mlsanity.com/paper/2308.13150/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Striking the Right Balance with Uncertainty](https://mlsanity.com/paper/1901.07590/) (2019)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias](https://mlsanity.com/paper/2305.19894/) (2023)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization](https://mlsanity.com/paper/2307.08347/) (2023)
- [Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls](https://mlsanity.com/paper/2202.01337/) (2022)
- [Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification](https://mlsanity.com/paper/1902.10946/) (2019)
- [Mini-DDSM: Mammography-based Automatic Age Estimation](https://mlsanity.com/paper/2010.00494/) (2020)
- [An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification](https://mlsanity.com/paper/2109.00201/) (2021)
- [Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network](https://mlsanity.com/paper/2208.00594/) (2022)
- [DenseNet for Breast Tumor Classification in Mammographic Images](https://mlsanity.com/paper/2101.09637/) (2021)
- [Breast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI](https://mlsanity.com/paper/2305.14389/) (2023)
- [Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning](https://mlsanity.com/paper/2305.20058/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Unified Scheme of ResNet and Softmax](https://mlsanity.com/paper/2309.13482/) (2023)
- [Mammo-SAE: Interpreting Breast Cancer Concept Learning with Sparse Autoencoders](https://mlsanity.com/paper/2507.15227/) (2025)
- [Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis](https://mlsanity.com/paper/2504.07775/) (2025)
- [Dual-attention ResNet outperforms transformers in HER2 prediction on DCE-MRI](https://mlsanity.com/paper/2510.13897/) (2025)

## Abstract (arXiv)

Automatic classification of breast cancer in histopathology images is crucial for accurate diagnosis and effective treatment planning. Recently, classification methods based on the ResNet architecture have gained prominence due to their ability to improve accuracy significantly. This is achieved by employing skip connections to mitigate vanishing gradient issues, enabling the integration of low-level and high-level feature information. However, the conventional ResNet architecture faces challenges such as data imbalance and limited interpretability, which necessitate cross-domain knowledge and collaboration among medical experts. To address these challenges, this study proposes a novel method for breast cancer classification: the Dual-Activated Lightweight Attention ResNet50 (DALAResNet50) model. This model integrates a pre-trained ResNet50 architecture with a lightweight attention mechanism, embedding an attention module in the fourth layer of ResNet50, and incorporates two fully connected layers with LeakyReLU and ReLU activation functions to enhance feature learning capabilities. Extensive experiments conducted on the BreakHis, BACH, and Mini-DDSM datasets demonstrate that DALAResNet50 outperforms state-of-the-art models in accuracy, F1 score, IBA, and GMean, particularly excelling in classification tasks involving imbalanced datasets. Furthermore, the proposed Dynamic Threshold Grad-CAM (DT Grad-CAM) method provides clearer and more focused visualizations, enhancing interpretability and assisting medical experts in identifying key features.