# Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning

arXiv: https://arxiv.org/abs/1811.02668 · HTML: https://mlsanity.com/paper/1811.02668/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy](https://mlsanity.com/paper/2306.07999/) (2023)
- [Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning](https://mlsanity.com/paper/2107.03602/) (2021)
- [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2201.08550/) (2022)
- [A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models](https://mlsanity.com/paper/2308.01328/) (2023)
- [Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes](https://mlsanity.com/paper/2301.05938/) (2023)
- [LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype](https://mlsanity.com/paper/2311.09574/) (2023)

## Abstract (arXiv)

Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging. However, they were limited to just predicting positive or negative finding for a specific neoplasm. We attempted to use Deep Learning with a convolutional neural network algorithm to build a lymphoma diagnostic model for four diagnostic categories: benign lymph node, diffuse large B cell lymphoma, Burkitt lymphoma, and small lymphocytic lymphoma. Our software was written in Python language. We obtained digital whole slide images of Hematoxylin and Eosin stained slides of 128 cases including 32 cases for each diagnostic category. Four sets of 5 representative images, 40x40 pixels in dimension, were taken for each case. A total of 2,560 images were obtained from which 1,856 were used for training, 464 for validation and 240 for testing. For each test set of 5 images, the predicted diagnosis was combined from prediction of 5 images. The test results showed excellent diagnostic accuracy at 95% for image-by-image prediction and at 10% for set-by-set prediction. This preliminary study provided a proof of concept for incorporating automated lymphoma diagnostic screen into future pathology workflow to augment the pathologists' productivity.